Publications (37) View all
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Article: Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): from microscopic measurements to macroscopic predictions of clinical progression.
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ABSTRACT: Ductal carcinoma in situ (DCIS)--a significant precursor to invasive breast cancer--is typically diagnosed as microcalcifications in mammograms. However, the effective use of mammograms and other patient data to plan treatment has been restricted by our limited understanding of DCIS growth and calcification. We develop a mechanistic, agent-based cell model and apply it to DCIS. Cell motion is determined by a balance of biomechanical forces. We use potential functions to model interactions with the basement membrane and amongst cells of unequal size and phenotype. Each cell's phenotype is determined by genomic/proteomic- and microenvironment-dependent stochastic processes. Detailed "sub-models" describe cell volume changes during proliferation and necrosis; we are the first to account for cell calcification. We introduce the first patient-specific calibration method to fully constrain the model based upon clinically-accessible histopathology data. After simulating 45 days of solid-type DCIS with comedonecrosis, the model predicts: necrotic cell lysis acts as a biomechanical stress relief and is responsible for the linear DCIS growth observed in mammography; the rate of DCIS advance varies with the duct radius; the tumour grows 7-10mm per year--consistent with mammographic data; and the mammographic and (post-operative) pathologic sizes are linearly correlated--in quantitative agreement with the clinical literature. Patient histopathology matches the predicted DCIS microstructure: an outer proliferative rim surrounds a stratified necrotic core with nuclear debris on its outer edge and calcification in the centre. This work illustrates that computational modelling can provide new insight on the biophysical underpinnings of cancer. It may 1-day be possible to augment a patient's mammography and other imaging with rigorously-calibrated models that help select optimal surgical margins based upon the patient's histopathologic data.Journal of Theoretical Biology 02/2012; 301:122-40. · 2.21 Impact Factor -
Article: Metastatic neuroendocrine tumour in the breast: a potential mimic of in-situ and invasive mammary carcinoma.
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ABSTRACT: The aim of this study was to review the clinicopathological characteristics of neuroendocrine tumours (NETs) metastasizing to the breast, in order to identify features that could be useful in distinguishing these metastatic lesions from primary breast neoplasms. Eighteen metastatic NETs in the breast were identified from two large hospitals over a 15-year period. Eleven (62%) tumours originated in the gastrointestinal tract, 5 (28%) originated in the lung, and the other two were of indeterminate origin. Eight (44%) cases were initially misdiagnosed as primary mammary carcinomas. In retrospect, all metastatic tumours exhibited architectural and cytological features that would suggest neuroendocrine differentiation. Immunohistochemistry can further aid in the distinction between metastatic neuroendocrine and primary mammary carcinoma. All 11 tumours from the gastrointestinal tract expressed CDX-2, 3 (60%) of five tumours from the lung expressed thyroid transcription factor-1, and only 2 (11%) of 18 showed weak oestrogen receptor positivity. Additionally, unlike primary carcinomas, the majority (82%) of metastatic NETs were negative for cytokeratin 7, and all were negative for gross cystic disease fluid protein 15 and mammoglobin. There is a high propensity for metastatic NETs to mimic primary breast carcinomas. Careful attention to cytological and architectural features can help to identify cases that require further immunophenotypic workup with a panel of tissue-specific antibodies. However, clinical history is paramount for optimal diagnosis.Histopathology 10/2011; 59(4):619-30. · 3.08 Impact Factor -
Article: A novel, patient-specific mathematical pathology approach for assessment of surgical volume: Application to ductal carcinoma in situ of the breast.
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ABSTRACT: We introduce a novel "mathematical pathology" approach, founded on a biophysical model, to identify robust patient-specific predictors of tumor growth useful in clinical practice to improve the accuracy of diagnosis/prognosis and intervention. In accordance with biological observations, our model simulates the diffusion-limited in situ tumors with a relatively short phase of fast initial growth, followed by a prolonged slow-growth phase where tumor size is constrained primarily by the relative weight of cell mitosis and death. The former phase may only last for a few months, so that at the time of diagnosis, we may assume that most tumors will have entered the phase where their size is changing slowly. Based on this prediction, we hypothesize that the volume of breast with ducts affected by in situ tumors at the time of diagnosis will be closely approximated by a model-derived mathematical function based on the ratio of tumor cell proliferation-to-apoptosis indices and on the extent of diffusion of cell nutrients (diffusion penetration length), which can be measured from immunohistochemical and morphometric analysis of patient histopathology specimens without the need for multiple-time measurements. We tested this idea in a retrospective study of 17 patients by staining breast tumor specimens containing ductal carcinoma in situ for mitosis with Ki-67 and for apoptosis with cleaved caspase-3 and counting cells positive for each marker. We also determined diffusion penetration by measuring the thickness of viable rims of tumor cells within ducts. Using the ensuing ratios, we applied the model to determine a predicted surgical volume or tumor size. We then corroborated our hypothesis by comparing the predicted size of each tumor based on our model with the actual size of the pathological specimen after tumor excision (R2 = 0.74-0.88). In addition, for the 17 cases studied, both histological grade and mammography were not found to correlate with tumor size (R2 = 0.08-0.47). We conclude that our mathematical pathology approach yields a high degree of accuracy in predicting the size of tumors based on the mitotic/apoptotic index and on diffusion penetration. By obtaining these ratios at the time of initial biopsy, pathologists can employ our model to predict the size of the tumor and thereby inform surgeons how much tissue to remove (surgical volume). We discuss how results from the model have implications concerning the current debate on recommendations for screening mammography, while the model itself may contribute to better planning of breast conservation surgery.Analytical cellular pathology (Amsterdam) 09/2011; · 0.92 Impact Factor -
Article: Multiplicity: an organizing principle for cancers and somatic mutations.
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ABSTRACT: With the advent of whole-genome analysis for profiling tumor tissue, a pressing need has emerged for principled methods of organizing the large amounts of resulting genomic information. We propose the concept of multiplicity measures on cancer and gene networks to organize the information in a clinically meaningful manner. Multiplicity applied in this context extends Fearon and Vogelstein's multi-hit genetic model of colorectal carcinoma across multiple cancers. Using the Catalogue of Somatic Mutations in Cancer (COSMIC), we construct networks of interacting cancers and genes. Multiplicity is calculated by evaluating the number of cancers and genes linked by the measurement of a somatic mutation. The Kamada-Kawai algorithm is used to find a two-dimensional minimum energy solution with multiplicity as an input similarity measure. Cancers and genes are positioned in two dimensions according to this similarity. A third dimension is added to the network by assigning a maximal multiplicity to each cancer or gene. Hierarchical clustering within this three-dimensional network is used to identify similar clusters in somatic mutation patterns across cancer types. The clustering of genes in a three-dimensional network reveals a similarity in acquired mutations across different cancer types. Surprisingly, the clusters separate known causal mutations. The multiplicity clustering technique identifies a set of causal genes with an area under the ROC curve of 0.84 versus 0.57 when clustering on gene mutation rate alone. The cluster multiplicity value and number of causal genes are positively correlated via Spearman's Rank Order correlation (rs(8) = 0.894, Spearman's t = 17.48, p < 0.05). A clustering analysis of cancer types segregates different types of cancer. All blood tumors cluster together, and the cluster multiplicity values differ significantly (Kruskal-Wallis, H = 16.98, df = 2, p < 0.05). We demonstrate the principle of multiplicity for organizing somatic mutations and cancers in clinically relevant clusters. These clusters of cancers and mutations provide representations that identify segregations of cancer and genes driving cancer progression.BMC Medical Genomics 06/2011; 4:52. · 3.69 Impact Factor -
Article: Selective genomic copy number imbalances and probability of recurrence in early-stage breast cancer.
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ABSTRACT: A number of studies of copy number imbalances (CNIs) in breast tumors support associations between individual CNIs and patient outcomes. However, no pattern or signature of CNIs has emerged for clinical use. We determined copy number (CN) gains and losses using high-density molecular inversion probe (MIP) arrays for 971 stage I/II breast tumors and applied a boosting strategy to fit hazards models for CN and recurrence, treating chromosomal segments in a dose-specific fashion (-1 [loss], 0 [no change] and +1 [gain]). The concordance index (C-Index) was used to compare prognostic accuracy between a training (n = 728) and test (n = 243) set and across models. Twelve novel prognostic CNIs were identified: losses at 1p12, 12q13.13, 13q12.3, 22q11, and Xp21, and gains at 2p11.1, 3q13.12, 10p11.21, 10q23.1, 11p15, 14q13.2-q13.3, and 17q21.33. In addition, seven CNIs previously implicated as prognostic markers were selected: losses at 8p22 and 16p11.2 and gains at 10p13, 11q13.5, 12p13, 20q13, and Xq28. For all breast cancers combined, the final full model including 19 CNIs, clinical covariates, and tumor marker-approximated subtypes (estrogen receptor [ER], progesterone receptor, ERBB2 amplification, and Ki67) significantly outperformed a model containing only clinical covariates and tumor subtypes (C-Index(full model), train[test] = 0.72[0.71] ± 0.02 vs. C-Index(clinical + subtype model), train[test] = 0.62[0.62] ± 0.02; p<10(-6)). In addition, the full model containing 19 CNIs significantly improved prognostication separately for ER-, HER2+, luminal B, and triple negative tumors over clinical variables alone. In summary, we show that a set of 19 CNIs discriminates risk of recurrence among early-stage breast tumors, independent of ER status. Further, our data suggest the presence of specific CNIs that promote and, in some cases, limit tumor spread.PLoS ONE 01/2011; 6(8):e23543. · 4.09 Impact Factor
