Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling

Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models

Clinical and Translational Implications of Centrosome Amplification and Clustering in Multiple Malignancies

Cancer initiation and progression are multistep processes that rely on the generation and accumulation of non-lethal mutations, which deregulate function of tumor suppressor genes and activate oncogenic pathways. Evolving through a landscape of heterogeneous somatic mutations, mutated cells undergo subsequent selection pressures and the one endowed with the greatest fitness advantage survives giving rise to genetically diverse cell populations resulting in intratumor heterogeneity (ITH). Presence of the abnormal number of centrosomes is one of the key factors contributing towards ITH. Clustering of amplified centrosomes allows cancer cells to avoid mitotic spindle multipolarity that could otherwise result in cell death either by mitotic catastrophe or a high-grade multipolar division yielding intolerably severe aneuploidy. Thus, centrosome clustering enables low-grade chromosomal missegregation and their unequal distribution to daughter cells resulting in chromosomal instability (CIN), thus contributing to neoplastic transformation. Owing to the presence of genetically different cells in a tumor, monotargeted therapy spares clones lacking therapy-specific targets giving them the opportunity to repopulate the tumor with immunity toward the applied therapy and propensity to recur. Therefore, ITH poses major challenges to both clinicians and drug developers as it precludes detection of low-level clones, prediction of tumor evolution, development of drugs to target specific clones and evaluation of effective, yet non-toxic combinatorial regimens to combat ITH. I envision that a comprehensive quantitative analysis of centrosome amplification (CA), which is a bonafide driver of ITH might help better understand clinical behavior and improve therapeutic management of tumors. To this end, my research, presented here, primarily focuses on testing i) the impact of centrosome amplification and centrosome clustering protein (KIFC1) on clinical outcomes in multiple malignancies and ii) the role of tumor hypoxia in inducing centrosome amplification in cancer. Collectively, my findings reveal that CA and KIFC1 are prognostic and predictive in multiple malignancies and that tumor hypoxia plays a crucial role in inducing CA in tumors. This body of work expands our knowledge in causes and clinical implications of CA to help guide treatment decisions and development of precision medicine for multiple malignancies

Unsupervised multiple kernel learning approaches for integrating molecular cancer patient data

Cancer is the second leading cause of death worldwide. A characteristic of this disease is its complexity leading to a wide variety of genetic and molecular aberrations in the tumors. This heterogeneity necessitates personalized therapies for the patients. However, currently defined cancer subtypes used in clinical practice for treatment decision-making are based on relatively few selected markers and thus provide only a coarse classifcation of tumors. The increased availability in multi-omics data measured for cancer patients now offers the possibility of defining more informed cancer subtypes. Such a more fine-grained characterization of cancer subtypes harbors the potential of substantially expanding treatment options in personalized cancer therapy. In this thesis, we identify comprehensive cancer subtypes using multidimensional data. For this purpose, we apply and extend unsupervised multiple kernel learning methods. Three challenges of unsupervised multiple kernel learning are addressed: robustness, applicability, and interpretability. First, we show that regularization of the multiple kernel graph embedding framework, which enables the implementation of dimensionality reduction techniques, can increase the stability of the resulting patient subgroups. This improvement is especially beneficial for data sets with a small number of samples. Second, we adapt the objective function of kernel principal component analysis to enable the application of multiple kernel learning in combination with this widely used dimensionality reduction technique. Third, we improve the interpretability of kernel learning procedures by performing feature clustering prior to integrating the data via multiple kernel learning. On the basis of these clusters, we derive a score indicating the impact of a feature cluster on a patient cluster, thereby facilitating further analysis of the cluster-specific biological properties. All three procedures are successfully tested on real-world cancer data. Comparing our newly derived methodologies to established methods provides evidence that our work offers novel and beneficial ways of identifying patient subgroups and gaining insights into medically relevant characteristics of cancer subtypes.Krebs ist eine der häufigsten Todesursachen weltweit. Krebs ist gekennzeichnet durch seine Komplexität, die zu vielen verschiedenen genetischen und molekularen Aberrationen im Tumor führt. Die Unterschiede zwischen Tumoren erfordern personalisierte Therapien für die einzelnen Patienten. Die Krebssubtypen, die derzeit zur Behandlungsplanung in der klinischen Praxis verwendet werden, basieren auf relativ wenigen, genetischen oder molekularen Markern und können daher nur eine grobe Unterteilung der Tumoren liefern. Die zunehmende Verfügbarkeit von Multi-Omics-Daten für Krebspatienten ermöglicht die Neudefinition von fundierteren Krebssubtypen, die wiederum zu spezifischeren Behandlungen für Krebspatienten führen könnten. In dieser Dissertation identifizieren wir neue, potentielle Krebssubtypen basierend auf Multi-Omics-Daten. Hierfür verwenden wir unüberwachtes Multiple Kernel Learning, welches in der Lage ist mehrere Datentypen miteinander zu kombinieren. Drei Herausforderungen des unüberwachten Multiple Kernel Learnings werden adressiert: Robustheit, Anwendbarkeit und Interpretierbarkeit. Zunächst zeigen wir, dass die zusätzliche Regularisierung des Multiple Kernel Learning Frameworks zur Implementierung verschiedener Dimensionsreduktionstechniken die Stabilität der identifizierten Patientengruppen erhöht. Diese Robustheit ist besonders vorteilhaft für Datensätze mit einer geringen Anzahl von Proben. Zweitens passen wir die Zielfunktion der kernbasierten Hauptkomponentenanalyse an, um eine integrative Version dieser weit verbreiteten Dimensionsreduktionstechnik zu ermöglichen. Drittens verbessern wir die Interpretierbarkeit von kernbasierten Lernprozeduren, indem wir verwendete Merkmale in homogene Gruppen unterteilen bevor wir die Daten integrieren. Mit Hilfe dieser Gruppen definieren wir eine Bewertungsfunktion, die die weitere Auswertung der biologischen Eigenschaften von Patientengruppen erleichtert. Alle drei Verfahren werden an realen Krebsdaten getestet. Den Vergleich unserer Methodik mit etablierten Methoden weist nach, dass unsere Arbeit neue und nützliche Möglichkeiten bietet, um integrative Patientengruppen zu identifizieren und Einblicke in medizinisch relevante Eigenschaften von Krebssubtypen zu erhalten

Quantitative Proteomic and Mutational Landscape of Metaplastic Breast Carcinoma and Generation of a 3D Organoid Model of Neoplastic Progression

Triple-negative breast cancer (TNBC) is considered to be the most aggressive and has worse prognosis compared to other breast cancers and accounts for roughly 18% of all epithelial cancers of the breast, or carcinomas. TNBC exhibits complex molecular heterogeneity both inter- and intratumorally and likely consists of several distinct molecular subgroups that are currently unknown. Metaplastic breast carcinoma (MBC) is even more aggressive than triple-negative breast cancer (TNBC) but also typically presents as triple-negative histologically, and is defined by the admixture of both invasive glandular and non-glandular “metaplastic” heterologous elements of spindle, squamous or sarcomatoid subtypes. The protein profiles underpinning the phenotypic diversity and metastatic behavior of MBC are unknown. We present a quantitative multi-subtype proteomic landscape of MBC, non-metaplastic TNBC, and normal breast from small yet well-annotated cohort of 27 patients, and also present the somatic mutational landscape on the same cohort. We used multiplex isobaric tandem mass tag (TMT) labeling for proteomics and quantified 5,798 proteins, and from whole-exome sequencing for genomics analysis we found 980 total somatic mutational variants. MBCs displayed increased epithelial-to-mesenchymal transition (EMT) and extracellular matrix (ECM) signaling, and reduced metabolic pathways compared to TNBC. We discovered subtype-specific profiles among MBCs including distinct upregulated profiles; translation and ribosomal events in spindle, inflammation and apical junctions in squamous, and extracellular matrix in sarcomatoid. Comparison of the proteomes of spindle MBC with MMTV-cre;Ccn6fl/fl spindle MBC mouse tumors revealed a shared spindle-specific signature of 17 upregulated proteins involved in translation (e.g. RPL4,6,18, P3H1, PYCR1). The somatic mutational landscape also revealed MBCs share common TP53 mutations, and in PLEC, MUC17, CRYBG2, and ZNF681. We identified that spindle and squamous MBC exhibit overlapping mutational profiles of genes involved in transcription, RNA metabolic processes and actin filament binding, while sarcomatoid tumors harbor distinct mutations in MAPK, WNT, protocadherin cluster genes, calcium binding and ECM organization. These data identify subtype-specific MBC protein profiles and mutational signatures that identified novel biomarkers for therapy. Three-dimensional (3D) cell culture has been widely used in recent decades, compared with monolayer (2D) culture, because they better mimic the in vivo state. 3D systems utilize different types of gels critical for their success, such as collagen or the reconstituted basement membrane, Matrigel, which has enabled recapitulation of tissue architecture and function that is more physiologic compared to 2D. However, conventional 3D models using gel-embedded platforms have large variability and slow transport of biomolecules to the matrix-encapsulated cells. Here, we developed a highly reproducible, 3D scaffold-free hanging drop method amenable for primary tissues including mouse and human tumors, and our analyses describe a one drop-one organoid format using MCF10A cells, a non-tumorigenic breast cell line. We attained high-yield production of uniform organoids that resemble normal human breast acini, express both mammary gland-specific and progenitor markers, and we developed treatment assays for EMT induction and neoplastic progression delivering rapid quantification of phenotypic and morphological changes. Integration of 3D methods with omics analyses is envisioned to enhance the study of neoplastic progression and generate novel targets of both MBC and TNBC tumors.PHDMolecular & Cellular PathologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155234/1/djomehri_1.pd