Abstract

Background

   Malignant peripheral nerve sheath tumors (MPNST) are rare highly
   aggressive sarcomas that affect 8-13% of people with neurofibromatosis
   type 1. The prognosis for patients with MPNST is very poor. Despite
   TOP2A overexpression in these tumors, doxorubicin resistance is common,
   and the mechanisms of chemotherapy resistance in MPNST are poorly
   understood. Molecular-guided therapy prediction is an emerging strategy
   for treatment refractory sarcomas that involves identification of
   therapy response and resistance mechanisms in individual tumors. Here,
   we report the results from a personalized, molecular-guided therapy
   analysis of MPNST samples.

Methods

   Established molecular-guided therapy prediction software algorithms
   were used to analyze published microarray data from human MPNST samples
   and cell lines, with benign neurofibroma tissue controls. MPNST and
   benign neurofibroma-derived cell lines were used for confirmatory in
   vitro experimentation using quantitative real-time PCR and growth
   inhibition assays. Microarray data was analyzed using Affymetrix
   expression console MAS 5.0 method. Significance was calculated with
   Welch’s t-test with non-corrected p-value < 0.05 and validated using
   permutation testing across samples. Paired Student’s t-tests were used
   to compare relative EC50 values from independent growth inhibition
   experiments.

Results

   Molecular guided therapy predictions highlight substantial variability
   amongst human MPNST samples in expression of drug target and drug
   resistance pathways, as well as some similarities amongst samples,
   including common up-regulation of DNA repair mechanisms. In a subset of
   MPNSTs, high expression of ABCC1 is observed, serving as a predicted
   contra-indication for doxorubicin and related therapeutics in these
   patients. These microarray-based results are confirmed with
   quantitative, real-time PCR and immunofluorescence. The functional
   effect of drug efflux in MPNST-derived cells is confirmed using in
   vitro growth inhibition assays. Alternative therapeutics supported by
   the molecular-guided therapy predictions are reported and tested in
   MPNST-derived cells.

Conclusions

   These results confirm the substantial molecular heterogeneity of MPNSTs
   and validate molecular-guided therapy predictions in vitro. The
   observed molecular heterogeneity in MPNSTs influences therapy
   prediction. Also, mechanisms involving drug transport and DNA damage
   repair are primary mediators of MPNST chemotherapy resistance.
   Together, these findings support the utility of individualized therapy
   in MPNST as in other sarcomas, and provide initial proof-of concept
   that individualized therapy prediction can be accomplished.

Background

   Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive
   sarcomas associated with substantial morbidity and mortality [[36]1].
   MPNSTs are rare in the general population, affecting about 1 in 100,000
   people each year [[37]2], whereas individuals with neurofibromatosis
   type 1 (NF1) carry an 8-13% lifetime risk of developing an MPNST
   [[38]1]. Despite aggressive, multi-modal treatment, overall survival is
   poor for both primary and metastatic MPNST [[39]1,[40]3].

   Chemotherapy resistance is a hallmark of both primary and recurrent
   MPNSTs [[41]4,[42]5] owing to a variety of factors, most notably
   up-regulation of drug efflux transporters [[43]4,[44]6-[45]8].
   Alternative mechanisms of chemotherapy resistance in MPNSTs and other
   sarcomas have been described, including Twist 1 overexpression [[46]9],
   Bcl-xl overexpression [[47]10], and autophagy induction [[48]11].
   Escalation of DNA repair processes is also observed in other
   chemotherapy-resistant sarcomas [[49]12-[50]14]. The doxorubicin
   target, topoisomerase II (TOP2A), is significantly overexpressed in
   MPNSTs [[51]15] compared to neurofibromas [[52]16]. Doxorubicin binds
   to the topoisomerase II complex following DNA strand breaks,
   interrupting cellular replication [[53]17]. However, overexpression of
   TOP2A is associated with diminished survival in MPNST, confirming that
   overexpression of the doxorubicin target is insufficient to overcome
   established mechanisms of doxorubicin resistance [[54]15].
   Doxorubicin-based chemotherapy regimens are typically used to treat
   MPNST, but the therapeutic benefit is modest and closely parallels that
   of other soft-tissue sarcoma regimens [[55]18,[56]19], and dose
   limiting toxicity is common [[57]20].

   The refractory nature of MPNSTs is attributable to a high degree of
   molecular heterogeneity, both in terms of mechanisms underlying disease
   progression [[58]21] and rapidly evolving therapy resistance. Studies
   confirm deletion or loss of function in tumor suppressor genes,
   including NF1, HMMR/RHAMM, TP53, and duplications or gain of function
   mutations in several oncogenes, including MET, HGF, EGFR, ITGB4, and
   PDGFRA [[59]22]. Other deregulated pathways in MPNSTs include a variety
   of well-characterized drug targets such as mTOR, HGF/Met, TOP2A, Ras,
   and steroid hormones [[60]15,[61]16,[62]22-[63]27].

   Molecular-guided therapy prediction or personalized medicine (PMED)
   strategies are currently under evaluation for use in recurrent and
   refractory pediatric brain tumors ([64]NCT01802567), neuroblastoma
   ([65]NCT01355679) and sarcomas ([66]NCT01772771). This approach is also
   a promising treatment alternative for therapy-resistant cancers like
   MPNST [[67]28-[68]30]. PMED workflows follow a knowledge and
   rules-based statistical algorithm that converts genomic profiling data
   into an ordinal ranking of therapies. Drug predictions are therefore
   agnostic to disease context and adaptable to a variety of clinical
   scenarios. Essential to the PMED drug prediction algorithm is the
   reconciliation of predicted therapies selected from a comprehensive
   drug list against known mechanisms of chemotherapy resistance and drug
   resistance biomarkers. This knowledge-based rules approach relies on
   databases, such as DrugBank, that feature annotated references to over