The current study demonstrates that, using an innovative phenotypic PGx model applied to CTICs isolated from a single blood sample, patients with advanced PDAC can be divided into three treatment response groups. This profile is predictive of progression-free survival, and a trend for overall survival. Serial measurement of SMAD4 expression is also predictive of treatment response and resistance.

is one of the four most commonly mutated genes seen in PDAC, along withand. Mutational rate seen in a recent large sequencing effort was 35% [ 21 ].is a classical tumor suppressor gene, and acts to regulate the transforming growth factor-β (TGF-β) signaling pathway [ 22 ]. Evidence is accumulating associatingloss with poor prognosis in PDAC, therefore,expression is a biomarker of great interest in PDAC.

Beyond predicting effective therapy, clinicians are also without tools or biomarkers that can anticipate treatment response or resistance in individual patients with PDAC. The most commonly used circulating biomarker for monitoring PDAC is the Sialyl Lewis A antigen, carbohydrate antigen (CA) 19-9. CA 19-9 has limited prognostic power in PDAC. Low and decreasing CA 19-9 levels following surgical resection [ 16 17 ] have been shown to correlate with survival. In patients with locally advanced PDAC, a decrease of >90% in CA 19-9 level following chemoradiotherapy has been shown in one study to be associated with improved survival [ 18 ]. In patients with advanced PDAC, however, CA 19-9 is a poor predictor of survival and results show heterogeneous outcomes. One large randomized study found that a 50% decrease in CA 19-9 after two months of chemotherapy treatment or at CA 19-9 nadir did not predict for longer survival in advanced PDAC [ 19 ]. Furthermore, CA 19-9 serum level is limited by poor sensitivity, false negative results in Lewis negative phenotype (5–10%), and increased false positivity in the presence of obstructive jaundice (10–60%) [ 20 ].

In a previous study using a different drug sensitivity approach, gene expression profiling of CTICs was shown to predict effective therapy in advanced PDAC [ 15 ]. This study was conducted in a cohort of patients (= 50) treated predominantly with 5-FU based chemotherapy, prior to the FDA approval of G/nab-P, which has now become a widely used standard frontline treatment for advanced PDAC.

Translating a tumor-based drug prediction model to a blood-based assay is attractive and feasible. A clinical assay based on circulating cells found in peripheral blood has several advantages over one based on tumor tissue. Peripheral blood can be sampled repeatedly over time conveniently and safely, unlike tumor tissue. Circulating cells found in peripheral blood may provide unique information regarding cancer growth and metastasis not present in any individual tumor. We have built a robust platform for capturing and preserving circulating cells from 6 mL of heparinized blood drawn peripherally from patients with PDAC. This approach has been previously shown to successfully isolate circulating tumor cells (CTCs) with tumorigenic properties (CD45-EpCAM+CK+ and the ability to degrade and ingest collagenous matrices) in breast [ 7 ], prostate [ 8 ], and ovarian [ 9 ] cancers. Circulating tumor cells isolated in this fashion have been shown to correlate numerically with cancer stage [ 7 ] and prognosis [ 9 ]. Furthermore, these CTCs have been shown to reflect the genomics of the primary tumor [ 8 ], including presence ofmutation in CTCs isolated from PDAC patients [ 10 ]. Not all captured cells express these markers typical of classical tumor cells, but all cells isolated in this manner have the ability to invade into cell-adhesion matrices. In a prior study, CTCs made up between 0.03 to 0.07% of the cells isolated [ 10 ]. This modified cell invasion assay isolates classical CTCs [ 11 ] and invasive immune cells (EPCAM(−) mesenchymal cells, and invasive immune cells), hence the term circulating tumorigenic and invasive cells (CTICs) [ 12 14 ].

An alternative approach is based on the connectivity map concept [ 5 ]. Connectivity mapping hypothesizes that biological systems with similar gene expression profiles share biological properties important for drug response. The connectivity mapping approach has been validated, for example, effectively predicting rapamycin induced glucocorticoid sensitivity in acute lymphoblastic leukemia [ 6 ]. We hypothesize that gene expression profiles of response to anticancer agents can be created by comparing the expression patterns of model systems, such as cell lines, with divergent drug responses. Tumors with gene expression profiles similar to drug resistant profiles will be resistant to treatment, and those similar to drug sensitive profiles will be responsive.

Clinicians remain without tools for predicting which of these chemotherapeutic agents will be most effective for treating individual patients with PDAC. In other diseases, tumor tissue-based biomarkers have been identified as predictive of drug effect. Typically, this strategy has been effective where the biomarker is linked to the mechanism of action of the agent, i.e., Her2 expression and trastuzumab or activating EGFR mutation and erlotinib. Similar strategies have generally not been successful in PDAC, largely due to the absence of active, targeted therapies. A recent example attempting to predict response to cytotoxic chemotherapy involved the human equilibrative nucleoside transporter-1 (hENT1), a transporter protein thought important for cellular uptake of gemcitabine. Preliminary studies suggested low expression of hENT1 could result in gemcitabine resistance; however, prospective validation did not confirm these findings in patients with advanced disease [ 4 ].

Pancreatic ductal adenocarcinoma (PDAC) currently represents the 3rd leading cause of cancer mortality in the U.S. Of the five most lethal cancers, incidence and death rates are only increasing for PDAC. Therefore, it is estimated that by 2020, PDAC is likely to rise to the 2nd leading cause of cancer death in the U.S. [ 1 ]. Despite this, the emergence of active combination chemotherapy regimens during the past three years has led to incremental improvements in overall survival. Combination chemotherapy regimens, such as 5-fluorouracil (5-FU), leucovorin, irinotecan, oxaliplatin (FOLFIRINOX) [ 2 ], and gemcitabine with nab-paclitaxel (G/nab-P) [ 3 ] represent clinically meaningful improvements over the prior standard of care, single agent gemcitabine, for patients with advanced PDAC.

Combining Δand PGx platforms, patients with profiles predicting response to G/nab-P and+ (S4+/GN+) experienced impressive PFS of 11.6 months, much longer than any of the other subgroups in the analysis (between 4.1 and 5.9 months,< 0.0001, see Figure 3 B). Using 6-month PFS as a cutoff, this combined profile (S4+/GN+) has a PPV of 100%, NPV of 67%, with a sensitivity of 79% and specificity of 100% (see Figure 2 D).

Longitudinalmeasurements were obtained for 21 patients. Measurements were made from CTICs obtained prior to treatment and after 8–12 weeks of treatment. Increase inexpression, termed+, was seen in 14 patients, and decrease expression, termed-, was seen in seven patients.+ patients experienced significantly longer PFS compared to- patients (7.9 mo versus 5.5 mo, HR = 0.39, log-rank test,= 0.0233, Figure 3 A). Using 6-month PFS as a cutoff,+ had a PPV of 71%, NPV 57%, with a sensitivity of 77% and specificity of 50% (see Figure 2 C). Baseline level ofexpression was not predictive of PFS, and tumor tissueexpression was not available. Historically, change in CA 19-9 was used as a biomarker of disease response to chemotherapy. Baseline and change in CA 19-9 from baseline to after 8–12 weeks of treatment were analyzed. Baseline CA 19-9 was not predictive of PFS. Using thresholds of 50% and 90% CA 19-9 decrease, no PFS difference was seen ( Figure 3 C,D). Combining Δand CA 19-9 was not predictive of PFS.

Median overall survival (OS) for all evaluable patients in this study was 12.5 months. A trend for OS difference was seen based on G/nab-P sensitivity or resistance (median OS 12.6 vs. 9.8 months, p = 0.07). Of note, the vast majority of patients who went on to second line therapy (17/18, 94%) received 5-fluorouracil based chemotherapy.

Analysis was performed based on G/nab-P sensitivity or resistance. Patients with predicted G/nab-P sensitivity demonstrated a median PFS of 7.8 vs. 5.2 months for those with predicted resistance (= 0.0021, see Figure 1 B). Using 6-month PFS as a cutoff, G/nab-P drug sensitivity prediction has a PPV of 72%, NPV of 67%, with a sensitivity of 76% and specificity of 62% (see Figure 2 B).

Median PFS for all evaluable patients in this study was 7 months. We hypothesized that patients with profiles predicting drug sensitivity to G/nab-P would experience the longest PFS; patients with profiles predicting drug sensitivity to FOLFIRINOX, and therefore resistance to G/nab-P based on our preclinical modeling, would experience the shortest PFS; and patients with profiles predicting intermediate drug sensitivity would experience an intermediate PFS. Significant differences in PFS were in fact seen in patients from the three PGx profile groups. Median PFS for the PGx profile groups G/nab-P, intermediate and FOLFIRINOX groups were 8.7, 6.3, and 5.2 months, respectively (see Figure 1 A). Comparing PFS across the three groups, log-rank test for trend was statistically significant,= 0.0064. Comparing the G/nab-P to FOLFIRINOX groups only, PFS was significantly longer in the G/nab-P group (= 0.0031, hazard ratio of 0.34, log-rank test). Six-month PFS (PFS-6) is a commonly used surrogate endpoint for assessing drug response [ 23 24 ]. Using PFS-6 as a cutoff, PGx profile grouping had a positive predictive value (PPV) of 72%, negative predictive value (NPV) of 55%, sensitivity of 72%, and specificity of 55% (see Figure 2 A).

A PGx model was developed (see Materials and Methods). A prospective clinical trial was conducted to validate this model. All 37 patients enrolled in the study received frontline treatment with G/nab-P. Seven patients were excluded from analysis due to early discontinuation of treatment prior to restaging, either due to treatment toxicity (= 3) or death (= 4). All PDAC patient blood samples drawn prior to frontline treatment could be matched to one of three distinct drug sensitivity profiles: G/nab-P, intermediate or FOLFIRINOX. A strong negative correlation was seen between profiles predicting sensitivity to G/nab-P and FOLFIRINOX (see Figure S1 , R= 0.74,= 0.0001). At baseline, 40% (12/30) of evaluable patients were predicted to be most sensitive to G/nab-P, 23% (7/30) of intermediate sensitivity or 37% (11/33) most sensitive to FOLFIRINOX (see Table 1 for patient demographics). With respect to the G/nab-P regimen specifically, 57% (17/30) patients were predicted to have sensitivity, 43% (13/30) resistance. Patient characteristics were balanced across phenotypic profiles. Patient age was significantly positively associated with both progression free (PFS) and overall survival (OS), however, no association was seen between age and PGx prediction.

From a single 6 mL heparinized whole blood sample, CTICs could be isolated, greater than 1.0 ng/μL of RNA extracted and PGx profiling successfully performed in >95% of the samples analyzed. Analysis failure could be attributed to errors in qPCR, typically isolated to failure of a single gene to amplify, bad passive dye readings and exponential algorithm failures. Errors in EPCAM+ cell isolation were attributed to sample degradation during shipment.

3. Discussion

Advanced PDAC remains incurable; however, the development of combination chemotherapy regimens such as G/nab-P [ 3 ] and FOLFIRINOX [ 2 ] has resulted in more effective treatment options. Clinicians remain without effective tools for choosing the most active treatment regimen and for anticipating disease progression for individual patients. Researchers also require new and effective tools for screening new drugs. The current study supports PGx profiling of CTICs as a promising new tool for addressing these unmet needs.

The CTICs represent a mixed population of cells including classical EPCAM(+) CTCs, EPCAM(−) mesenchymal cells, and invasive immune cells. While isolation of pure, circulating tumor cells for study is worthwhile, such cells are present at exceedingly low levels, particularly in patients with PDAC. Nevertheless, a growing body of literature supports the utility of a mixed, CTIC population, particularly for predicting drug therapy in patients. For example, Pearl and colleagues, using a similar invasion assay in ovarian cancer, recently demonstrated that direct chemotherapy treatment of patient-derived CTICs in vitro accurately predicted in vivo response [ 11 ]. Our current study supports the intriguing concept that invasive cells present in circulation, the majority of which do not express markers consistent with classical CTCs, can be used to predict treatment response. CTICs merit further study to better understand the composition of this cell population and their biology. In PDAC, gene expression profiling rather than direct drug treatment of CTICs has been shown to be a reasonable surrogate for predicting drug response in vivo [ 14 ].

KRAS genetically engineered mouse (GEM) model of PDAC has accelerated the screening of drug candidates for clinical testing [ One approach to predicting drug response in PDAC is to focus only on PDAC derived models [ 25 ]. Development of the oncogenicgenetically engineered mouse (GEM) model of PDAC has accelerated the screening of drug candidates for clinical testing [ 26 ]. It has become apparent, however, that tissue of origin is not sufficient for predicting drug responses in patients, with drugs targeting smoothened (SMO) and heparin sulfate showing promise in GEM models, but ultimately failing in the clinic. New, innovative patient-derived organoid (PDO) models may offer a way forward, capturing the complexity of the human disease [ 27 ]. Our recent work shows that drug responses in PDOs parallel the response of patients from whom they are derived. Gene expression profiles of PDOs can be generated which also can predict response to some drugs in patients.

Our current study is based on the premise that gene expression pathways relevant to drug response are independent of, and more predictive than, tissue of origin. This hypothesis builds upon prior work using the NCI-60 cell line panel to model drug sensitivity based on common genes and pathways across tumor types. Scherf and colleagues previously demonstrated that a simple cluster analysis could segregate the NCI-60 cell lines primarily based on their tissue of origin; however, when cell lines were clustered based on growth inhibition of 1400 drugs, clustering differed greatly, based on gene expression profiles [ 28 ]. Cell lines with common tissues of origin often demonstrated disparate drug responses. Strong mechanistic rationale was found for the genes predictive of drug response, for example, dihydropyrimidine dehydrogenase expression and 5-FU, and asparagine synthetase (ASNS) expression and L-asparaginase. Staunton and colleagues [ 29 ] further explored this approach, using the NCI-60 cell lines to study response to 232 drugs. A training group of cell lines were used to generate profiles of sensitivity and resistance. The profiles were then tested in a separate validation group. Profiles were designed independent of tissue of origin. Eighty-eight of the 232 profiles accurately predicted drug sensitivity. Lee and colleagues [ 30 ] further extended this approach, using the NCI-60 panel to generate a novel gene expression algorithm termed coexpression extrapolation, or COXEN, to predict drug sensitivity. The algorithm was accurate for predicting drug response in breast cancer patients, and cisplatin response in an independent panel of 40 bladder cancer cell lines. Importantly, bladder cancer, like pancreatic cancer, is not a tumor type included in the NCI-60 panel.

ABC superfamily gene members (including ABCB1 , ABCC1 , ABCC2 and ABCG1 ) known to act as active drug transporters to reduce accumulation of chemotherapy drugs within resistant cancer cells [34,35, ABC and SLC genes have been linked to chemotherapy resistance in colon cancer [ SLC28A1 and SLC29A1 in PDAC tumor tissue have been best studied and are associated with gemcitabine response [ The current study demonstrates the effectiveness of a platform which is innovative and distinct from this previously developed and published platform. In the prior study, drug sensitivity profiles were developed and matched to patient CTIC gene expression profiles using gene-set enrichment analysis. The current study uses an innovative nearest template prediction approach (Adera Biolabs, Germantown, MD, USA). The previously described assay was based on a standard microarray platform, whereas the current study utilizes qPCR, which is regarded as the gold standard for measuring RNA expression [ 31 32 ]. The microarray approach has several advantages such as the ability to measure expression of thousands of genes simultaneously and reasonable accuracy. Disadvantages include high background signal due to cross-hybridization and limited dynamic range of detection. At the time that the expression profiles were generated from the NCI-60 panel, currently available next-generation RNA sequencing (RNAseq) was not available. RNAseq allows for more reliable quantitation of large numbers of transcripts, compared with microarray approaches, and is the basis of our current PDO profiling work. The current PGx assay profiles expression of more than 80 genes not assessed in previous studies (propriety to Adera BioLabs, Germantown, MD); the primary role of these genes is to transport drugs and other molecules. Several of the genes in the current profile have been characterized, typically in cell lines or tumor tissue, and have been found to play an important role in response and resistance to cytotoxic chemotherapeutic drugs. Our current study includes assessment of selectsuperfamily gene members (includingand) known to act as active drug transporters to reduce accumulation of chemotherapy drugs within resistant cancer cells [ 33 36 ]. More recently, solute carrier (SLC) genes have been studies as important genes governing drug sensitivity and resistance. In total, 400 SLC genes have been identified, acting to transport a variety of molecules across the plasma membrane or in intracellular organelles [ 36 37 ]. Germline polymorphisms in bothandgenes have been linked to chemotherapy resistance in colon cancer [ 38 ]. Expression ofandin PDAC tumor tissue have been best studied and are associated with gemcitabine response [ 39 ]. Other members of the SLC family have been studied to a lesser extent and association with drug response is largely unknown.

Our results represent the first set of studies demonstrating that expression profiling of a panel of these genes in CTICs can predict treatment response. In patients with advanced PDAC, our PGx model identifies three groups with differential response to cytotoxic chemotherapy regimens. In an independent group of patients with advanced PDAC, all of whom were treated with G/nab-P chemotherapy, patients predicted to respond to G/nab-P experienced significantly longer PFS compared to patients predicted not to respond to G/nab-P. While there is a trend to longer OS in the G/nab-P sensitive group, this does not reach statistical significance; possible explanations include the small size of the study, and the high percentage of patients who received 5-FU based chemotherapy in the second line. For patients predicted to be resistant to G/nab-P, sensitivity to 5-FU based chemotherapy could account for similar OS. A prospective study, using our assay to direct chemotherapy treatment in multiple lines of therapy, is needed to confirm PFS and OS benefits, and is underway.

Profiling and drug response prediction can be performed on a single 6 mL blood sample. The current study, focused on patients receiving G/nab-P chemotherapy, a recently developed front-line standard therapy for advanced PDAC, together with results of a prior study, which enrolled patients receiving a variety of different chemotherapy regimens, primarily built upon a 5-FU backbone [ 15 ], supports the concept of PGx profiling of CTICs as a clinically useful assay to help clinicians choose effective chemotherapeutic regimens for patients with advanced PDAC.

SMAD4 is a biomarker of great interest and has been studied extensively in PDAC tissue. One early study described patterns of metastases at time of autopsy in 76 patients with PDAC and that loss of SMAD4 was highly correlated with the presence of a widespread metastasis [ SMAD4 expression in PDAC tumors was associated with poor overall survival [ SMAD4 allele leads to local disease progression but not distant metastases, and further loss of SMAD4 heterozygosity leads to increase of both local and metastatic potential [ SMAD4 expression has also been shown to be important for anti-tumor activity in circulating immune cells such as T cells [ SMAD4 in CTICs also is a predictor of poor prognosis and outperforms CA 19-9 as a predictor of treatment response and resistance. With the emergence of new biomarker candidates, including a variety of nucleic acids (circulating DNA, lncRNA and miRNA [ is a biomarker of great interest and has been studied extensively in PDAC tissue. One early study described patterns of metastases at time of autopsy in 76 patients with PDAC and that loss ofwas highly correlated with the presence of a widespread metastasis [ 40 ]. A recent meta-analysis of 1762 patients from 14 studies concluded that loss ofexpression in PDAC tumors was associated with poor overall survival [ 41 ]. Intriguingly, a genetically engineered mouse model suggests loss of oneallele leads to local disease progression but not distant metastases, and further loss ofheterozygosity leads to increase of both local and metastatic potential [ 42 ].expression has also been shown to be important for anti-tumor activity in circulating immune cells such as T cells [ 43 ] and NK cells [ 44 ]. Further mechanistic studies are warranted. The current study is the first evidence that diminished expression ofin CTICs also is a predictor of poor prognosis and outperforms CA 19-9 as a predictor of treatment response and resistance. With the emergence of new biomarker candidates, including a variety of nucleic acids (circulating DNA, lncRNA and miRNA [ 45 ], exosomes and proteins (thrombospondin-2 [ 46 ]), investigation into their role in predicting treatment response is warranted.