We seek to understand the evolving needs of people who are faced with a life-changing medical diagnosis based on analyses of queries extracted from an anonymized search query log. Focusing on breast cancer, we manually tag a set of Web searchers as showing disruptive shifts in focus of attention and long-term patterns of search behavior consistent with the diagnosis and treatment of breast cancer. We build and apply probabilistic classifiers to detect these searchers from multiple sessions and to detect the timing of diagnosis, using a variety of temporal and statistical features. We explore the changes in information-seeking over time before and after an inferred diagnosis of breast cancer by aligning multiple searchers by the likely time of diagnosis. We automatically identify 1700 candidate searchers with an estimated 90% precision, and we predict the day of diagnosis within 15 days with an 88% accuracy. We show that the geographic and demographic attributes of searchers identified with high probability are strongly correlated with ground truth of reported incidence rates. We then analyze the content of queries over time from searchers for whom diagnosis was predicted, using a detailed ontology of cancer-related search terms. Our analysis reveals the rich temporal structure of the evolving queries of people likely diagnosed with breast cancer. Finally, we focus on subtypes of illness based on inferred stages of cancer and show clinically relevant dynamics of information seeking based on dominant stage expressed by searchers.