Metastasis is the main cause of cancer morbidity and mortality. Despite its clinical significance, several fundamental questions about the metastatic process in humans remain unsolved. Does metastasis occur early or late in cancer progression? Do metastases emanate directly from the primary tumor or give rise to each other? How does heterogeneity in the primary tumor relate to the genetic composition of secondary lesions? Addressing these questions in representative patient populations is crucial, but has been difficult so far. Here we present a simple, scalable PCR assay that enables the tracing of tumor lineage in patient tissue specimens. Our methodology relies on somatic variation in highly mutable polyguanine (poly-G) repeats located in non-coding genomic regions. We show that poly-G mutations are present in a variety of human cancers. Using colon carcinoma as an example, we demonstrate an association between patient age at diagnosis and tumor mutational burden, suggesting that poly-G variants accumulate during normal division in colonic stem cells. We further show that poorly differentiated colon carcinomas have fewer mutations than well-differentiated tumors, possibly indicating a shorter mitotic history of the founder cell in these cancers. We collect multiple spatially separated samples from primary carcinomas and their metastases and use poly-G fingerprints to build well-supported phylogenetic trees that illuminate each patient's path of progression. Our results imply that levels of intra-tumor heterogeneity vary significantly among patients.

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the breast is a widely used non-invasive approach to gather information about the underlying physiology of breast tumors. Recent studies indicate that breast tumor heterogeneity may reflect the presence of different levels of cellular aggressiveness or habitats within the tumor. This heterogeneity has been correlated to the variations in the contrast enhancement patterns within the tumor apparent on gadolinium-enhanced DCE-MRI. Although pathological and qualitative (based on contrast enhancement patterns) studies suggest the presence of clini- cal and molecular predictive tumor sub-regions, this has not been fully investigated in the quantitative domain. The new era of cancer imaging emphasizes the use of Radiomics to provide in vivo quan- titative prognostic and predictive imaging biomarkers. Thus Radiomics focuses on apply- ing image analysis techniques to quantify tumor radiographic properties to create mineable databases from radiological images. In this research work, the Radiomics approach was ap- plied to develop a novel computer aided diagnosis (CAD) model for quantifying intratumor heterogeneity not only within the tumor as a whole, but also within tumor habitats with an intent to build predictive models in breast cancer. The process of building these predictive models started with 2-D tumor segmentation followed by habitat extraction (based on vari- ations in contrast patterns and geometry) and textural kinetic feature extraction to quantify habitat heterogeneity. A new correlation based random subspace ensemble framework was developed to evaluate the textural kinetics from the individual tumor habitats. This new CAD framework was applied to predict two clinical and prognostic factors: Axillary lymph node (ALN) metastases and Estrogen receptor (ER) status. An AUC of more than 0.8 was achieved for classifying breast tumors based on number of ALN involvement. The highest AUC of 0.91 was achieved for classifying tumors with no ALN metastases from tumors with 4 or more ALN metastases. For classifying tumors based on ER status the highest AUC of 0.87 was achieved. These results were acquired by utilizing the textural kinetic features from the tumor habitat with rapid delayed washout. The results presented in this work showed that the heterogeneity within the tumor habitats which showed rapid contrast washout in the delayed phase, correlated with aggressive cellular phenotypes. This work hypothesizes that successfully quantifying these prognostic factors will prove to be clinically significant as it can improve the diagnostic accuracy. This, in turn, will im- prove the breast cancer treatment paradigm by providing more tailored treatment regimens for aggressive tumors.

Die Intra-Tumor-Heterogenität spiegelt eine kontinuierliche Entwicklung zwischen den Zellen eines einzelnen Tumors wider. Sie ist eine der Hauptursachen für Arzneimittelresistenz bei der Krebsbehandlung. Um dieses Problem anzugehen, ist es daher wichtig, die Tumorevolution innerhalb eines einzelnen Patienten zu verstehen und erfolgreich zu modellieren. Bisherige Arbeiten haben sich nicht erfolgreich mit der Evolution von Tumoren befasst, deren Treiber strukturelle Veränderungen im Genom sind, wie z. B. somatische Kopienzahlveränderungen (SCNAs). Diese Arbeit befasst sich mit der Herausforderung, die Tumorevolution als Folge solcher Veränderungen zu charakterisieren. Wir verwenden einen phylogenetischen Ansatz zur Analyse von multiregionalen Datensätzen in einer großen Pan-Krebs-Kohorte. Wir untersuchen häufige SCNAs in verschiedenen Stadien der Tumorentwicklung und führen eine neue Methode, MEDICC2, ein, die die Tumorevolution innerhalb eines einzelnen Patienten rekonstruiert. In dieser Arbeit haben wir häufige SCNAs charakterisiert, die früh in der Tumorentwicklung auftreten. Aufgrund der Struktur der Kohorte ist die Charakterisierung der subklonalen SCNAs nicht eindeutig. Unsere neue Methode, MEDICC2, akzeptiert höhere Kopienzahlzustände und berücksichtigt die Verdopplung des gesamten Genoms, ein häufiges Ereignis in Tumoren, was eine genauere Modellierung der Tumorevolution ermöglicht. / Intra-tumor heterogeneity reflects an ongoing evolution among cells of a single tumor. It is one of the leading causes of drug resistance in cancer treatments. Therefore, to address this issue, it is important to understand and successfully model tumor evolution within a single patient. Previous work has failed to successfully address the evolution of tumors whose drivers are structural changes in the genome, such as somatic copy number alterations (SCNAs). This work addresses the challenge of characterizing tumor evolution as a result of such changes. We use a phylogenetic approach to analyze multi-region datasets in a large pan-cancer cohort. We investigate frequent SCNAs at different stages of tumor development, and introduce a new method, MEDICC2, which reconstructs tumor evolution within a single patient. In this work, we characterized frequent SCNAs that occur early in tumor development. Due to the structure of the cohort, the characterization of subclonal SCNAs remains inconclusive. Our new method, MEDICC2, accepts higher copy number states and takes into account whole-genome doubling, a frequent event in tumors, which allows for a more precise modeling of tumor evolution.