MOLECULAR DETERMINANTS OF RESPONSE TO ANTIANGIOGENIC THERAPIES IN PRECLINICAL MODELS OF HEAD AND NECK SQUAMOUS CELL CARCINOMA

Abstract Background Head and neck squamous cell carcinoma (HNSCC) is the eighth leading cancer by incidence worldwide. In the past 5 decades there have been significant advances in surgery and chemoradiotherapy, but very little improvement in survival rates. Hence, there is a pressing need to develop new therapeutic strategies in HNSCC. Antiangiogenic therapy represents a promising strategy in at least a subset of patients. Currently, there are no reliable predictive and resistance biomarkers to identify those patients most likely to benefit. Studies using relevant preclinical models that identify mechanisms of resistance to antiangiogenic agents will help meet these challenges. Principal Findings In this dissertation, we established preclinical models of intrinsic and acquired resistance to anti-VEGF antibody bevacizumab and identified potential biomarkers of drug response. To characterize mechanisms of intrinsic resistance, we evaluated the angiogenic profile of HNSCC cells from bevacizumab-sensitive and -resistant tumor models using antibody array. We showed that resistant cells expressed higher levels of proangiogenic factors including interleukin-8 (IL-8). We identified PI3K and IL-1α signaling as the molecular basis for overexpression of IL-8. Downregulation of IL-8 resulted in sensitization of resistant tumors to bevacizumab. Overexpression of IL-8 in sensitive tumors conferred resistance to bevacizumab. Serum analysis of HNSCC patients treated with a bevacizumab-containing regime indicated high baseline IL-8 levels in a subset of patients refractory to treatment but not in responders. In a novel xenograft model of acquired resistance, human-specific microarray analysis revealed upregulation of angiogenesis-related genes including fibroblast growth factor-2 (FGF2), fibroblast growth factor receptor-3 (FGFR3), phospholipase C gamma-2 (PLCg2), frizzled receptor-4 (FZD4), chemokine [C-X3-C motif] ligand-1 (CX3CL1), and chemokine [C-C motif] ligand-5 (CCL5). Upstream genes PLCg2, FZD4, CX3CL1, and CCL5 regulated increased expression of FGF2 via increased extracellular signal-regulated kinase (ERK) signaling. Co-targeting VEGF and FGFR sensitized resistant tumors to bevacizumab. Conclusions and Significance Our work has identified two distinct molecular mechanisms of resistance to bevacizumab in preclinical HNSCC models. IL-8 signaling mediated intrinsic resistance while upregulation of FGF signaling in response to anti-VEGF therapy contributed to acquired resistance. Above findings provide a mechanistic rationale for co-targeting these pathways in future clinical trials to enhance the therapeutic efficacy of antiangiogenic therapy

Targeted Mutation Detection in Advanced Breast Cancer Using MammaSeq Identifies RET as a Potential Contributor to Breast Cancer Metastasis

The lack of any reported breast cancer specific diagnostic NGS tests inspired the development of MammaSeq, an amplicon based NGS panel built specifically for use in advanced breast cancer. In a pilot study to define the clinical utility of the panel, 46 solid tumor samples, plus an additional 14 samples of circulating-free DNA (cfDNA) from patients with advanced breast cancer were sequenced and analyzed using the OncoKB precision oncology database. We identified 26 clinically actionable variants (levels 1-3) annotated by the OncoKB precision oncology database, distributed across 20 out of 46 solid tumor cases (40%), and 4 clinically actionable mutations distributed across 4 samples in the 14 cfDNA sample cohort (29%). The mutation allele (MAF) frequencies of ESR1-D538G and FOXA1-Y175C mutations correlated with CA.27.29 levels in patient-matched blood, indicating that MAF may be a reliable marker for disease burden. Interestingly, 4 of the mutations found in metastatic samples occurred in the gene RET, an oncogenic receptor tyrosine kinase. In an orthogonal study, the lab has recently identified RET as one of the most recurrently upregulated genes in breast cancer brain metastases. Interestingly, the ligand for RET is the family of glial-cell derived neurotrophic factors (GDNF), a growth factor secreted by glial cells of the central nervous system. This lead to the hypothesis that RET overexpression facilitates breast cancer brain metastasis in response to the high levels of GDNF, while RET activating point mutations increase metastatic capacity without specific organ tropism. While the effect of GDNF treatment on proliferation in 2D was limited, in ultra-low attachment (ULA) plates we saw a significant increase in anchorage independent growth of MCF-7 cells. To determine if GDNF acts as a chemoattractant for RET positive BrCa cells, we utilized a transwell migration assay, with GDNF as the sole chemoattractant. When RET was overexpressed, there was a visual increase in cell migration. Together, these studies demonstrate the clinical feasibility of using MammaSeq to detect clinically actionable mutations in breast cancer patients, and provides provisional data supporting the investigation of RET signaling as a potentially targetable mediator of breast cancer brain metastasis

Circulating tumor cell phenotyping via high‐throughput acoustic separation

The study of circulating tumor cells (CTCs) offers pathways to develop new diagnostic and prognostic biomarkers that benefit cancer treatments. In order to fully exploit and interpret the information provided by CTCs, the development of a platform is reported that integrates acoustics and microfluidics to isolate rare CTCs from peripheral blood in high throughput while preserving their structural, biological, and functional integrity. Cancer cells are first isolated from leukocytes with a throughput of 7.5 mL h-1 , achieving a recovery rate of at least 86% while maintaining the cells' ability to proliferate. High-throughput acoustic separation enables statistical analysis of isolated CTCs from prostate cancer patients to be performed to determine their size distribution and phenotypic heterogeneity for a range of biomarkers, including the visualization of CTCs with a loss of expression for the prostate specific membrane antigen. The method also enables the isolation of even rarer, but clinically important, CTC clusters.Accepted versio

Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers

Background: Measures of spatial intratumor heterogeneity are potentially important diagnostic biomarkers for cancer progression, proliferation, and response to therapy. Spatial relationships among cells including cancer and stromal cells in the tumor microenvironment (TME) are key contributors to heterogeneity. Methods: We demonstrate how to quantify spatial heterogeneity from immunofluorescence pathology samples, using a set of 3 basic breast cancer biomarkers as a test case. We learn a set of dominant biomarker intensity patterns and map the spatial distribution of the biomarker patterns with a network. We then describe the pairwise association statistics for each pattern within the network using pointwise mutual information (PMI) and visually represent heterogeneity with a two-dimensional map. Results: We found a salient set of 8 biomarker patterns to describe cellular phenotypes from a tissue microarray cohort containing 4 different breast cancer subtypes. After computing PMI for each pair of biomarker patterns in each patient and tumor replicate, we visualize the interactions that contribute to the resulting association statistics. Then, we demonstrate the potential for using PMI as a diagnostic biomarker, by comparing PMI maps and heterogeneity scores from patients across the 4 different cancer subtypes. Estrogen receptor positive invasive lobular carcinoma patient, AL13-6, exhibited the highest heterogeneity score among those tested, while estrogen receptor negative invasive ductal carcinoma patient, AL13-14, exhibited the lowest heterogeneity score. Conclusions: This paper presents an approach for describing intratumor heterogeneity, in a quantitative fashion (via PMI), which departs from the purely qualitative approaches currently used in the clinic. PMI is generalizable to highly multiplexed/hyperplexed immunofluorescence images, as well as spatial data from complementary in situ methods including FISSEQ and CyTOF, sampling many different components within the TME. We hypothesize that PMI will uncover key spatial interactions in the TME that contribute to disease proliferation and progression

Targeted mutation detection in breast cancer using MammaSeq™

Abstract Background Breast cancer is the most common invasive cancer among women worldwide. Next-generation sequencing (NGS) has revolutionized the study of cancer across research labs around the globe; however, genomic testing in clinical settings remains limited. Advances in sequencing reliability, pipeline analysis, accumulation of relevant data, and the reduction of costs are rapidly increasing the feasibility of NGS-based clinical decision making. Methods We report the development of MammaSeq, a breast cancer-specific NGS panel, targeting 79 genes and 1369 mutations, optimized for use in primary and metastatic breast cancer. To validate the panel, 46 solid tumors and 14 plasma circulating tumor DNA (ctDNA) samples were sequenced to a mean depth of 2311× and 1820×, respectively. Variants were called using Ion Torrent Suite 4.0 and annotated with cravat CHASM. CNVKit was used to call copy number variants in the solid tumor cohort. The oncoKB Precision Oncology Database was used to identify clinically actionable variants. Droplet digital PCR was used to validate select ctDNA mutations. Results In cohorts of 46 solid tumors and 14 ctDNA samples from patients with advanced breast cancer, we identified 592 and 43 protein-coding mutations. Mutations per sample in the solid tumor cohort ranged from 1 to 128 (median 3), and the ctDNA cohort ranged from 0 to 26 (median 2.5). Copy number analysis in the solid tumor cohort identified 46 amplifications and 35 deletions. We identified 26 clinically actionable variants (levels 1–3) annotated by OncoKB, distributed across 20 out of 46 cases (40%), in the solid tumor cohort. Allele frequencies of ESR1 and FOXA1 mutations correlated with CA.27.29 levels in patient-matched blood draws. Conclusions In solid tumor biopsies and ctDNA, MammaSeq detects clinically actionable mutations (OncoKB levels 1–3) in 22/46 (48%) solid tumors and in 4/14 (29%) of ctDNA samples. MammaSeq is a targeted panel suitable for clinically actionable mutation detection in breast cancer