No abstract

The anaplastic lymphoma kinase (ALK) gene plays an important physiologic role in the development of the brain and can be oncogenically altered in several malignancies, including non-small-cell lung cancer (NSCLC) and anaplastic large cell lymphomas (ALCL). Most prevalent ALK alterations are chromosomal rearrangements resulting in fusion genes, as seen in ALCL and NSCLC. In other tumors, ALK copy-number gains and activating ALK mutations have been described. Dramatic and often prolonged responses are seen in patients with ALK alterations when treated with ALK inhibitors. Three of these—crizotinib, ceritinib, and alectinib—are now FDA approved for the treatment of metastatic NSCLC positive for ALK fusions. However, the emergence of resistance is universal. Newer ALK inhibitors and other targeting strategies are being developed to counteract the newly emergent mechanism(s) of ALK inhibitor resistance. This review outlines the recent developments in our understanding and treatment of tumors with ALK alterations.

The insulin-like growth factor (IGF) ligands stimulate cellular proliferation and survival by activating the type I insulin-like growth factor receptor (IGF-IR). As a result, the IGF signaling system is implicated in a number of cancers, including those of the breast, prostate, and lung. In addition to mitogenic and anti-apoptotic roles that may directly influence tumor development, IGF-IR also appears to be a critical determinant of response to numerous cancer therapies. This review describes the role of the IGF-IR pathway in mediating resistance to both general cytotoxic therapies, such as radiation and chemotherapy, and targeted therapies, such as tamoxifen and trastuzumab. It concludes with a description of approaches to target IGF-IR and argues that inhibition of IGF signaling, in conjunction with standard therapies, may enhance the response of cancer cells to multiple modalities.

Purpose: We previously reported an insulin-like growth factor (IGF) gene expression signature, based on genes induced or repressed by IGF-I, which correlated with poor prognosis in breast cancer. We tested whether the IGF signature was affected by anti-IGF-I receptor (IGF-IR) inhibitors and whether the IGF signature correlated with response to a dual anti-IGF-IR/insulin receptor (InsR) inhibitor, BMS-754807.Experimental Design: An IGF gene expression signature was examined in human breast tumors and cell lines and changes were noted following treatment of cell lines or xenografts with anti-IGF-IR antibodies or tyrosine kinase inhibitors. Sensitivity of cells to BMS-754807 was correlated with levels of the IGF signature. Human primary tumorgrafts were analyzed for the IGF signature and IGF-IR levels and activity, and MC1 tumorgrafts were treated with BMS-754807 and chemotherapy.Results: The IGF gene expression signature was reversed in three different models (cancer cell lines or xenografts) treated with three different anti-IGF-IR therapies. The IGF signature was present in triplenegative breast cancers (TNBC) and TNBC cell lines, which were especially sensitive to BMS-754807, and sensitivity was significantly correlated to the expression of the IGF gene signature. The TNBC primary human tumorgraft MC1 showed high levels of both expression and activity of IGF-IR and IGF gene signature score. Treatment of MC1 with BMS-754807 showed growth inhibition and, in combination with docetaxel, tumor regression occurred until no tumor was palpable. Regression was associated with reduced proliferation, increased apoptosis, and mitotic catastrophe.Conclusions: These studies provide a clear biological rationale to test anti-IGF-IR/InsR therapy in combination with chemotherapy in patients with TNBC.

The development of resources for clinical interpretation of cancer-associated genetic alterations has significantly lagged behind the technical developments enabling their detection in a time-and cost-efficient manner. The lack of scientific and informatics decision support for oncologists can lead to no action being taken or suboptimal therapeutic choices being made, which could affect the clinical outcome of a patient as well as convoluting research findings from clinical trials. In this article, we describe the precision oncology decision support (PODS) platform developed within The Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy (IPCT) at MD Anderson Cancer Center; the platform aims to bridge the gap between molecular alteration detection and identification of appropriate treatments.

With the increasing availability of genomics, routine analysis of advanced cancers is now feasible. Treatment selection is frequently guided by the molecular characteristics of a patient's tumor, and an increasing number of trials are genomically selected. Furthermore, multiple studies have demonstrated the benefit of therapies that are chosen based upon the molecular profile of a tumor. However, the rapid evolution of genomic testing platforms and emergence of new technologies make interpreting molecular testing reports more challenging. More sophisticated precision oncology decision support services are essential. This review outlines existing tools available for health care providers and precision oncology teams and highlights strategies for optimizing decision support. Specific attention is given to the assays currently available for molecular testing, as well as considerations for interpreting alteration information. This article also discusses strategies for identifying and matching patients to clinical trials, current challenges, and proposals for future development of precision oncology decision support. .

Insulin and insulin-like growth factor I (IGF1) influence cancer risk and progression through poorly understood mechanisms. To better understand the roles of insulin and IGF1 signaling in breast cancer, we combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array, we measured the levels of 134 proteins in 21 breast cancer cell lines stimulated with IGF1 or insulin for up to 48 h. We then constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. Insulin (Ins)1 and type I insulin-like growth factor (IGF1) receptors (InsR and IGF1R, respectively) are receptor tyrosine kinases that are expressed in almost all types of cells. Signaling through InsR and IGF1R initiates a phosphorylation cascade that drives cell growth and proliferation (1-8). Overexpression of these receptors is correlated with higher breast cancer risk (9 -15) and has been shown to influence tumorigenesis, metastasis, and resistance to existing forms of cancer therapy (10,16,17). IGF receptor blockade can slow tumor growth and metastasis, but the receptor has proven to be difficult to target specifically (18 -20). A confounding factor in developing therapies targeting these receptors is their high sequence (ϳ60%) and structural homology. IGF1R and InsR are able to form functional hybrids, and each can partially compensate for the loss or suppression of the other (1-4, 21-24). Moreover, it has been shown that IGF1R signaling is one mechanism of resistance to conventional hormonal therapy (19,(25)(26)(27)(28)(29). Understanding the relationships between IGF1R and InsR signaling cascades and their combinatorial role in cancer is crucial to developing better diagnostics and personalized treatments for cancer.

High throughput genomic and molecular profiling of tumors is emerging as an important clinical approach. Molecular profiling is increasingly being utilized to guide cancer patient care, especially in advanced and incurable cancers. However, navigating the scientific literature to make evidenced-based clinical decisions based on molecular profiling results is overwhelming for many oncology clinicians and researchers. The Personalized Cancer Therapy website (www.personalizedcancertherapy.org) was created to provide an online resource for clinicians and researchers to facilitate navigation of available data. Specifically, this resource can be used to help identify potential therapy options for patients harboring oncogenic genomic alterations. Herein, we describe how content on www.personalizedcancertherapy.org is generated and maintained. We end with case scenarios to illustrate the clinical utility of the website. The goal of this publically-available resource is to provide easily-accessible information to a broad oncology audience, as this may help ease the information retrieval burden facing participants in the precision oncology field.