Stanley E. Shackney

Molecular evolutionary patterns in breast cancer.

Geno-phenotypic patterns of pre-invasive and invasive lobular breast cancers and infiltrating ductal carcinomas of low, intermediate, and high grade are reviewed. One of the main differences between lobular breast cancers and ductal carcinomas is the presence of inactivating E-cadherin gene mutations in lobular breast cancers. In many other respects, lobular breast cancers and low-grade ductal carcinomas exhibit similar geno-phenotypic profiles. The development of p53 dysfunction may be a hallmark of infiltrating ductal cancers of intermediate and high grade. Sequential Her-2/neu and ras abnormalities define a subset of aggressive high-grade tumors, and the development of Rb dysfunction may define a separate subset of aggressive ductal cancers. Based on these observations, a branching molecular evolutionary model for the development and progression of breast cancer is proposed.

Growth rates of small cell bronchogenic carcinomas

In reviewing a series of 144 patients with small cell bronchogenic carcinoma, 12 were found to have serially measurable roentgenographic lesions prior to therapy. Although caliper‐based measurements and a silhouette cutout method gave comparable sets of tumor doubling time data, inter‐observer variability was less with the silhouette cutout method. Tumor doubling times in small cell bronchogenic carcinoma ranged between 25 and 160 days, with a median of 77 days, a log mean of 81 days, and an arithmetic mean of 91 days. There was no apparent relation between tumor doubling time and tumor location, histologic subtype, response to therapy, or patient survival. The data indicate that small cell bronchogenic carcinoma of the lung is a relatively slowly growing tumor. Assuming that late subclinical disease exhibits growth characteristics that are similar to those seen in the clinical stages of growth, it can be estimated that residual body tumor burdens of 1 × 106 cells may be followed by tumor recurrence times of 2 years or longer; the likelihood of “cure” should not be entertained in patients with disease‐free intervals shorter than 4—5 years. Cancer 42:2880–2886, 1978.

Response to trastuzumab, erlotinib, and bevacizumab, alone and in combination, is correlated with the level of human epidermal growth factor receptor-2 expression in human breast cancer cell lines

Human epidermal growth factor receptor-2 (HER2) and epidermal growth factor receptor (EGFR) heterodimerize to activate mitogenic signaling pathways. We have shown previously, using MCF7 subcloned cell lines with graded levels of HER2 expression, that responsiveness to trastuzumab and AG1478 (an anti-EGFR agent), varied directly with levels of HER2 expression. HER2 and EGFR up-regulate vascular endothelial growth factor (VEGF), a growth factor that promotes angiogenesis and participates in autocrine growth-stimulatory pathways that might be active in vitro. Here, we show that trastuzumab, erlotinib, and bevacizumab, individually and in combination, inhibit cell proliferation in a panel of unrelated human breast cancer cell lines, in proportion to their levels of HER2 expression. The combination of all three drugs provided a greater suppression of growth than any single drug or two-drug combination in the high HER2–expressing cell lines (P < 0.001). Combination index analysis suggested that the effects of these drugs in combination were additive. The pretreatment net level of VEGF production in each cell line was correlated with the level of HER2 expression (r = 0.883, P = 0.016). Trastuzumab and erlotinib each reduced total net VEGF production in all cell lines. Multiparameter flow cytometry studies indicated that erlotinib alone and the triple drug combination produced a prolonged but reversible blockade of cells in G1, but did not increase apoptosis substantially. These studies suggest that the effects of two and three-drug combinations of trastuzumab, erlotinib, and bevacizumab might offer potential therapeutic advantages in HER2-overexpressing breast cancers, although these effects are of low magnitude, and are likely to be transient. [Mol Cancer Ther 2007;6(10):2664–74]

Phylogenetic analysis of multiprobe fluorescence in situ hybridization data from tumor cell populations.

Motivation: Development and progression of solid tumors can be attributed to a process of mutations, which typically includes changes in the number of copies of genes or genomic regions. Although comparisons of cells within single tumors show extensive heterogeneity, recurring features of their evolutionary process may be discerned by comparing multiple regions or cells of a tumor. A useful source of data for studying likely progression of individual tumors is fluorescence in situ hybridization (FISH), which allows one to count copy numbers of several genes in hundreds of single cells. Novel algorithms for interpreting such data phylogenetically are needed, however, to reconstruct likely evolutionary trajectories from states of single cells and facilitate analysis of tumor evolution. Results: In this article, we develop phylogenetic methods to infer likely models of tumor progression using FISH copy number data and apply them to a study of FISH data from two cancer types. Statistical analyses of topological characteristics of the tree-based model provide insights into likely tumor progression pathways consistent with the prior literature. Furthermore, tree statistics from the resulting phylogenies can be used as features for prediction methods. This results in improved accuracy, relative to unstructured gene copy number data, at predicting tumor state and future metastasis. Availability: Source code for software that does FISH tree building (FISHtrees) and the data on cervical and breast cancer examined here are available at ftp://ftp.ncbi.nlm.nih.gov/pub/FISHtrees. Contact: sachowdh@andrew.cmu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Robust unmixing of tumor states in array comparative genomic hybridization data

Motivation: Tumorigenesis is an evolutionary process by which tumor cells acquire sequences of mutations leading to increased growth, invasiveness and eventually metastasis. It is hoped that by identifying the common patterns of mutations underlying major cancer sub-types, we can better understand the molecular basis of tumor development and identify new diagnostics and therapeutic targets. This goal has motivated several attempts to apply evolutionary tree reconstruction methods to assays of tumor state. Inference of tumor evolution is in principle aided by the fact that tumors are heterogeneous, retaining remnant populations of different stages along their development along with contaminating healthy cell populations. In practice, though, this heterogeneity complicates interpretation of tumor data because distinct cell types are conflated by common methods for assaying the tumor state. We previously proposed a method to computationally infer cell populations from measures of tumor-wide gene expression through a geometric interpretation of mixture type separation, but this approach deals poorly with noisy and outlier data. Results: In the present work, we propose a new method to perform tumor mixture separation efficiently and robustly to an experimental error. The method builds on the prior geometric approach but uses a novel objective function allowing for robust fits that greatly reduces the sensitivity to noise and outliers. We further develop an efficient gradient optimization method to optimize this ‘soft geometric unmixing’ objective for measurements of tumor DNA copy numbers assessed by array comparative genomic hybridization (aCGH) data. We show, on a combination of semi-synthetic and real data, that the method yields fast and accurate separation of tumor states. Conclusions: We have shown a novel objective function and optimization method for the robust separation of tumor sub-types from aCGH data and have shown that the method provides fast, accurate reconstruction of tumor states from mixed samples. Better solutions to this problem can be expected to improve our ability to accurately identify genetic abnormalities in primary tumor samples and to infer patterns of tumor evolution. Contact: tolliver@cs.cmu.edu Supplementary information:Supplementary data are available at Bioinformatics online.

HER2 expression as a potential marker for response to therapy targeted to the EGFR

Since human epidermal growth factor receptor 2 (HER2) is known to participate with the epidermal growth factor receptor (EGFR) in mitogenic signalling, we hypothesised that HER2 overexpression might indicate responsiveness to EGFR targeted therapies. MCF7 breast cancer cells transfected with the HER2 gene were subcloned to establish a set of genetically related cell lines expressing graded levels of HER2 by immunoblot analysis. The subcloned cell lines and parental MCF7 cells were characterised by their growth characteristics, and cell by cell patterns of EGFR, HER2 and HER3 expression as well as levels of phosphorylated mitogen-activated protein kinase (MAPK) and AKT by laser scanning cytometry (LSC). Growth inhibition assays were used to characterise response to EGFR targeted therapy, and to determine the relationship between therapeutic response and levels of tyrosine kinase expression. The levels of growth inhibition of AG1478 and of the AG1478-trastuzumab combinations were correlated with levels of HER2 expression among the different cell lines. Among EGFR, HER2 and HER3, HER2 overexpression was the best single predictive marker, but combinations of two markers provided additional predictive information.

RECONSTRUCTING TUMOR PHYLOGENIES FROM HETEROGENEOUS SINGLE-CELL DATA

Studies of gene expression in cancerous tumors have revealed that tumors presenting indistinguishable symptoms in the clinic can be substantially different entities at the molecular level. The ability to distinguish between these genetically distinct cancers will make possible more accurate prognoses and more finely targeted therapeutics, provided we can characterize commonly occurring cancer sub-types and the specific molecular abnormalities that produce them. We develop a new method for identifying these common tumor progression pathways by applying phylogeny inference algorithms to single-cell assays, taking advantage of information on tumor heterogeneity lost to prior microarray-based approaches. We combine this approach with expectation maximization to infer unknown parameters used in the phylogeny construction. We further develop new algorithms to merge inferred trees across different assays. We validate the expectation maximization method on simulated data and demonstrate the combined approach on a set of fluorescent in situ hybridization (FISH) data measuring cell-by-cell gene and chromosome copy numbers in a large sample of breast cancers. The results further validate the proposed computational methods by showing consistency with several previous findings on these cancers and provide novel insights into the mechanisms of tumor progression in these patients.

Algorithms to model single gene, single chromosome, and whole genome copy number changes jointly in tumor phylogenetics.

We present methods to construct phylogenetic models of tumor progression at the cellular level that include copy number changes at the scale of single genes, entire chromosomes, and the whole genome. The methods are designed for data collected by fluorescence in situ hybridization (FISH), an experimental technique especially well suited to characterizing intratumor heterogeneity using counts of probes to genetic regions frequently gained or lost in tumor development. Here, we develop new provably optimal methods for computing an edit distance between the copy number states of two cells given evolution by copy number changes of single probes, all probes on a chromosome, or all probes in the genome. We then apply this theory to develop a practical heuristic algorithm, implemented in publicly available software, for inferring tumor phylogenies on data from potentially hundreds of single cells by this evolutionary model. We demonstrate and validate the methods on simulated data and published FISH data from cervical cancers and breast cancers. Our computational experiments show that the new model and algorithm lead to more parsimonious trees than prior methods for single-tumor phylogenetics and to improved performance on various classification tasks, such as distinguishing primary tumors from metastases obtained from the same patient population.

Complete response of carcinosarcoma of the ovary to therapy with doxorubicin, ifosfamide, and dacarbazine

Although carcinosarcoma occurs in various locations throughout the body, it rarely originates in the ovary. Chemotherapy has been minimally beneficial. This case describes a patient with carcinosarcoma of the ovary who responded minimally to chemotherapy used for epithelial carcinomas but had a complete response after receiving chemotherapy used for sarcomas. The patient relapsed within 1 year after receiving cisplatin therapy. She was treated with mesna, ifosfamide, Adriamycin, and dacarbazine (MAID) chemotherapy and after one cycle of chemotherapy she had no evidence of tumor. She has received six cycles of chemotherapy without evidence of progression 13+ months since beginning MAID therapy. MAID chemotherapy may be useful in the treatment of carcinosarcoma of the ovary.

Network-Based Inference of Cancer Progression from Microarray Data

Cancer cells exhibit a common phenotype of uncontrolled cell growth, but this phenotype may arise from many different combinations of mutations. By inferring how cells evolve in individual tumors, a process called cancer progression, we may be able to identify important mutational events for different tumor types, potentially leading to new therapeutics and diagnostics. Prior work has shown that it is possible to infer frequent progression pathways by using gene expression profiles to estimate ldquodistancesrdquo between tumors. Here, we apply gene network models to improve these estimates of evolutionary distance by controlling for correlations among coregulated genes. We test three variants of this approach: one using an optimized best-fit network, another using sampling to infer a high-confidence subnetwork, and one using a modular network inferred from clusters of similarly expressed genes. Application to lung cancer and breast cancer microarray data sets shows small improvements in phylogenies when correcting from the optimized network and more substantial improvements when correcting from the sampled or modular networks. Our results suggest that a network correction approach improves estimates of tumor similarity, but sophisticated network models are needed to control for the large hypothesis space and sparse data currently available.