DEEP LEARNING AND MOLECULAR PATHWAYS IDENTIFY TRANSLATIONAL TARGETS IN HNC
Tumors of the upper aerodigestive tract are closely associated with the peripheral nervous system. This likely contributes to the complex heterogeneity often appreciated in neoplasms that originate in the oropharynx, nasopharynx, and larynx. The etiological risk factors classically implicated in these diseases include genetics, tobacco and alcohol use, and infection with oncogenic viruses and co-morbid immunosuppression (Ang et al. 2010; Leemans et al. 2011; Kreimer et al. 2013). The oropharynx accounts for approximately 60% of all newly diagnosed aerodigestive tract tumors where exposure to oncogenic viruses such as the human papillomavirus (HPV) plays a significant role in disease progression (Hillbertz et al. 2012).
Recent evidence points to a significant role of neurotrophin signals from the peripheral nervous system in normal and neoplastic trans-differentiation (Fan et al. 2004, Bronzetti et al. 2006, Nakamura et al. 2007). The low-affinity nerve growth factor (p75NTR; LNGFR; CD271) is the principal neurotrophin receptor that signals via the nerve growth factor (NGF) (Lu, Pang, Woo 2005). These receptors classically heterodimerize with tyrosine kinases (Trk) A,B, and C promoting a diversity of receptor/ligand induced signals. p75NTR and various
(BDNF); Glial cell-derived Neurotrophic Factor (GDNF); Neurotrophin 3 (NT-3) and Neurotrophin 4 (NT-4)) are active in maintaining the somatosensory
functions of the oropharynx (Bronzetti et al. 2006, others). Cancers of the head and neck are aggressive and often associate with the peripheral nervous system in perineural invasion and are associated with brain metastasis in as many at 6- 18% of all cases (Tse 2013; Ruzevick et al. 2013). Recent studies have
suggested an epigenetic role for TrkA and B and their cognate ligands in HNC that differentiates HPV-driven from HPV negative disease (Kostareli et al. 2013). Additionally, TrkB has been demonstrated to predict poor overall survival in oropharyngeal cancers and has been proposed as a target for clinical intervention (Kupferman et al. 2010; Lee J et al. 2012).
Taken together, the role that these neurogenesis-related drivers play in the pathogenesis of virally induced head and neck cancer might hold important insight into the molecular biology of the underlying tumors. We propose that these targets might provide a rationale for the involvement of the peripheral nervous system and offer potential targets for therapeutic intervention. 4.1 MATERIALS AND METHODS
Patient Specimen Collection
All patient specimens were collected under informed consent of the patient and the approval of the institutional review boards of the Medical University of
DNA, RNA, and Protein Expression Analysis
DNA, RNA, and Protein were collected as previously described (Tomar et al. 2016). Briefly, flash frozen or RNA-later preserved surgical specimens were de-identified and cryostored at -80 C and -20 C, respectively. Nucleic acids and protein were collected using All Prep as per manufacturer’s protocols (Qiagen). Nucleic acids and proteins were assessed for quality and quantity by
spectrophotometer for all downstream analyses. cDNA was generated using iScript reverse-transcriptase for all patient samples (BioRad, USA).
HPV-Serotyping and Activity Analysis
All samples were typed for HPV-DNA using a linearized reverse
hybridization assay as previously described (INNO-LiPa, Fujirebio) as previously described. Briefly, gDNA was globally amplified using primers directed to the L1 region od 27 HPV types. The resulting amplimers were then hybridized to linear indicators and results were quantified. HPV-Activity was assessed by
quantitative real-time PCR (qPCR) as described in Tomar et al. 2014. Briefly, primer sequences for HPV16 E7 (Forward: 5’ — CCG GAC AGA GCC CAT TAC AAT – 3’ 5’ — CCG GAC AGA GCC CAT TAC AAT – 3’; Reverse: 5’ — ACG TGT GTG CTT TGT ACG CAC – 3’) were utilized to amplify cDNA synthesized on the template of tumor-derived RNA (Lamarcq et al. 2002). qPCR conditions were as follows: initial denaturation (95°C x 180 s.) followed by 40 iterative cycles of denaturation (95°C x 10 s) and primer annealing and elongation, respectively (60s).
Agilent cDNA Microarray Analysis
Microarray experiments were performed using the Agilent Technologies (Santa Clara, CA) platform. Total RNA samples were amplified and labeled using Agilent’s Low Input Quick Amp Labeling Kit according to the manufacturer’s recommendations. Briefly, mRNA contained in 200 ng of total RNA was converted into cDNA using a poly-dT primer that also contained the T7 RNA polymerase promoter sequence. Subsequently, T7 RNA polymerase was added to cDNA samples to amplify the original mRNA molecules and to simultaneously incorporate cyanine-3 labeled CTP into the amplification product (cRNA). In the next step, labeled cRNA molecules were purified using Qiagen’s RNeasy Mini Kit (Valencia, CA). After spectrophotometric assessment of dye incorporation and cRNA yield, samples were stored at −80°C until hybridization. Labeled cRNA samples (600 ng) were hybridized to SurePrint G3 Human Gene Expression 8×60K v2 Microarrays at 65°C for 17 h, using Agilent’s Gene Expression Hybridization Kit according to the manufacturer’s recommendations. After washes, arrays were scanned using a High Resolution Agilent DNA Microarray Scanner and images saved in TIFF format.
Deep Learning Validation of Microarray Gene Expression
Data were extracted from images with Feature Extractor Software version 10.7.3.1 (Agilent) where background correction was also performed.
base line transformed using the median of all samples. Then, data were filtered by flags in a way that 75% of the samples in at least one of the treatment groups have a “detected” flag. Differentially expressed genes were determined by
analysis of the data using the non-parametric Mann-Whitney unpaired test. Cutoff values of 0.02 and 2 were used for p-value and fold-change, respectively. Gene ontology (GO) pathway analysis and hierarchical cluster analysis were also performed with GeneSpring. For the hierarchical cluster analysis, Euclidean similarity metrics and Wards linkage rule were used along with Kruskal-Wallis non-parametric ANOVA. Kaplan-Meier survival curves were generated using GraphPad PRISM version 6.0 (GraphPad, Software, La Jolla, CA). A p-value of 0.05 or less was considered significant.
Immunohistochemistry Validation of HPV+ vs. HPV- Cancers
Formalin-fixed paraffin embedded (FFPE) samples were cut at 5 µm thickness on a microtome and antigen-recovered at 75 oC. Specimens were then
probed using antibodies to NGFR (Clinical Pre-dilute; Biocare), POU4F1 (1:200; Millipore) NEFH (1:1000; Biocare), Ki67 (1:2000, Cell Signaling Technologies). DAB chromogen was utilized for detection of antigen (Abcam). Freshly resected HNC tumor lesions were flash frozen and embedded in OCT mounting medium. Specimens were fixed in cold methanol, serially sectioned on a cryostat at 7 µm and incubated with anti-mouse NEFH-HRP (1:500; Biocare) to visualize
4.2 RESULTS
HPV Positive and HPV Negative Tumors Differentially Express
Neurogenesis-Related Genes. Craniofacial development occurs early in embryonic development and the connective tissue of the head and neck
originates from the neural crest. A stem cell niche of de-differentiated basal oral keratinocytes (bOK) are maintained to promote wound healing and tissue
regeneration throughout life (Jones & Klein 2013). This niche has been
suggested as the target of oncogenic HPV viruses within the oral mucosae and transformation and neoplasia are associated with poor differentiation of the epithelium (Syrjänen 2010). These slowly proliferating (Ki67lo) cells express the NGFR (p75NTRhi) and cytokeratins (CK 5/14/19) as well as other important developmentally regulated genes and are the targets of oncogenic
papillomaviruses (Leemans et al. 2011). We discovered a preponderance of known and novel neural-related genes in our data set when analyzing differences between HPV driven and HPV- tumors (Figure 4.1; Table 4.1). We further
assessed these neural-associated genes in the Walter et al. TCGA data-set and discovered a significant concordance in expression (Figure 4.2) (Walter et al., 2013). These data led us to further consider the role HPV16 infection might play in de-differentiated bOKs and activation of neural pathways in head and neck cancer.
an important mechanism in the pathogenesis in HNC. We chose to focus our analysis on the peripheral nervous system (PNS) and gene components of the sympathetic and somatosensory compartments. We found significant differences when comparing HPV16 positive versus HPV16 negative expression which
included GABA receptor Pi (GABRP (40 Fold)), Glutamic acid decarboxylase 1 (GAD1(10 Fold)) Single-minded (SIM2 (9.8 Fold)), NGFR (p75NTR (6.7 Fold)), POU4F1 (POU4F1 (6.2 Fold)), neurofilament heavy chain (NEFH; 200 kDa) in HPV positive and HPV patients amongst others (Figure 4.3).
Anatomic Markers of Peripheral Nerves are Up-regulated in HPV+ HNC pathology. To validate these gene expression changes in HPV+ cancers, we next examined the expression of NEFH. Because NEFH was up-regulated in our original dataset in HPV+ versus HPV- cancers and it is considered a gold-
standard immunohistochemical marker for peripheral nerves, we performed IHC staining on HPV+ versus HPV- tumor samples. Utilizing clinical-grade
antibodies, we stained for NEFH and observed a statistically significant increase in the number of cases that were positive for NEFH in HPV+ versus HPV-
cancers (42% versus 22%, respectively; p<0.05%, t test, Figure 4.4A). Furthermore, our IHC study indicated an increase in peritumoral invasion microanatomy in HPV+ HNC (Figure 4.4B) versus HPV- cancers.
Nerve growth factor receptor (NGFR) is Up-regulated in HPV+ HNC. To begin to determine a functional role and to validate machine learning data, we next evaluated the expression of the low-affinity neurotrophic receptor, NGFR.
validated antibodies to assess whether gene expression changes observed were present in HPV+ versus HPV- cancers. Our data demonstrated a marked
difference in the prevalence of NGFR expression in HPV+ HNC versus HPV- cancers (75% versus 8%, respectively; Figure 4.5A. p=0.0078).
These factors have previously been demonstrated to play a role in HNC (p75NTR), HPV-associated malignancies (POU4F1), and esophageal cancers (NEFH), respectively. We observed an abundance of targets downstream of critical neural crest transcription factors (see schematic representation in Figure 4.6). Together, these data suggested that these neural-related genes might be targets in the pathogenesis of virally induced HNC.
4.3 TABLES AND FIGURES
Table 4.1. Selected neurogenesis-related genes up-regulated > 2.0 fold in HPV+ versus HPV- HNC.
Gene FC vs. HPV-
Neural Crest Markers
POU4F1 6.02
ISL1 10.24
Perineural Invasion Markers PTN 7.58 MDK 2.85 SRGAP3 5.34 Pain-Receptors TRPV6 5.27
Figure 4.1. HPV+ and HPV- Tumors have distinct gene expression profiles. Unsupervised hierarchical clustering demonstrates divergent gene expression profiles in HPV+ vs. HPV- tumors p<0.02; FC ≥ 2 Mann-Whitney.
HPV
-n=16
HPV
+Figure 4.2. Key neural crest genes are mutated in an independent TCGA HNC dataset. (A) Screenshot from TCGA HNC dataset implicates NGF activation in P75NTR. (B) Mutations in HPV+ HNC that are significant for NGFR and TrkA signaling and are associated with the neurotrophin signaling cascade. p<0.05; FC ≥ 2 Mann-Whitney, Student’s t-Test.
Figure 4.3. Neurogenesis-related genes associated with neurotrophin signaling, neurotransmitter signaling, and neuritogenesis are significantly overexpressed in HPV+ cancers versus HPV- cancers.
0.0
0.5
1.0
1.5
2.0
2.5
KRT19
KCNS1
GABRPTLX3
NEURL3ISL1
GAD1
KCNA2LHX2
KCNB2SIM2
GRIN2CCBLN2
NGFR
POU4F1IRX6
GAD1
MYCN
TRAF1TLX2
NEDD4LPCLO
NMU
GRM8
log FC
7E-04 2E-04 3E-04 2E-03 5E-04 4E-03 4E-04 5E-04 4E-03 3E-03 3E-03 2E-04 2E-02 1E-02 3E-03 5E-04 9E-03 2E-03 7E-03 7E-03 4E-03 7E-04 2E-02 2E-03p value
HPV
+HPV
-A
B
HPV+ HPV- 0.0 0.2 0.4 0.6 0.8Percentage NEFH Positive Cases
%
Cases
Figure 4.5. HPV+ tumors have high NGFR expression more often than HPV- tumors (A) % of HPV+ and HPV- cancers with positive NGFR staining (Wilcoxon t-test p=0.0078) (B) 40x representative images of NGFR staining in HPV+ and
HPV+ HPV- 0.0 0.2 0.4 0.6 0.8 1.0
NGFR+ Samples
%NG
F
R
+
**
HPV
+HPV
-B
A
Figure 4.6. Proposed mechanism for peritumoral neurogenesis and signaling via the neurotrophin receptor pathway.
Immune infiltrate Peritumoral
Neurites Tumor cells
Reactive Stroma NGFR TrkA PI3Km NF κb HPV Brn3a NGFR NGF proNGF v v v v NRAGE JNK IRAK SHC MAPK TRAF6 PTN, MDK NINJ1 cJun
4.4 DISCUSSION
The proximity of tumorigenic cells to nerves is universally associated with poor prognosis in head and neck cancers (Tse 2012). Neurotrophism-associated genes (such as NGF, BDNF, etc.) have been demonstrated to play a key role in neurogenic drive in the peripheral nervous system (PNS) (Kolokythas et al. 2010). In addition, it has been previously posited that HPV transformed HNC carry a proclivity toward neuroendocrine differentiation in a subset of patients (Kraft et al. 2012). However, the involvement of the PNS in tumorigenesis and metastasis remains a novel area of investigation. Ruzevick et al. recently demonstrated increased brain metastasis in HPV-positive head and neck cancers where the major anatomic site was the base of tongue (2013). The tongue is highly innervated by the branches of the trigeminal, facial,
glossopharyngeal, and vagus and has recently been demonstrated to be the most prevalent anatomic site in perineural invasion (this Thesis; Tai et al. 2012). Our data suggest that the virally-induced homeobox transcription factor POU4F1 plays a role downstream of HPV-infection in inducing p75NTR expression as well as other critical neurotrophism-related genes. Additionally, our data present the first mechanistic insight into the importance of p75NTR signaling in HPV-active versus HPV-negative. Interestingly, our data not only clearly support a role for NGF but also implicate downstream GABA and Glutamate signaling within the periphery. GAD1, the key synthetic enzyme that converts glutamate to GABA, has recently been shown to promote invasion and metastasis in oral cancers
(GRIN2C), glutamate receptor metabotrophic 8 (GRM8), and various aspects of the glutamate signaling cascade provide intriguing targets for further
investigation. The glutamatergic system has been implicated in GWAS studies by Vink et al. including these aforementioned targets which are increased in smokers versus non-smokers (Vink et al. 2009). It is well accepted that cancer patients on β-adrenergic blockers have lower rates of recurrence and diminished mortality (Barron et al. 2011; Melhem-Bertrandt et al. 2011; Lemeshow et al. 2011; Grytli et al. 2013; Grytli et al. 2013). Recent mechanistic evidence in prostate cancer suggests a dualism between the sympathetic and
parasympathetic disease (Magnon et al. 2013). Neurotrophins are expressed in abundance in saliva and play pleiotrophic roles in maintaining the
sympathetic/parasympathetic tempo within the upper aerodigestive tract (Laurent et al. 2013). Interestingly, acetylation of the H3 in proximity to POU4F1 has been suggested as a mechanism of control in regulating sensory ganglia development (Eng et al. 2007).
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