Rationale. Secondary to breast cancer and its treatment, fatigue has been identified as one of the most commonly reported symptoms by patients at all stages along the cancer continuum. In addition, sleep disruption has been shown to be notably elevated among cancer patients as compared to the general population. Sleep disruption and cancer-related fatigue have often been evaluated as components of larger symptom clusters, along with other cancer-related medical and psychosocial symptoms. While many studies have evaluated symptom clusters in breast cancer, few have examined symptom clusters that consider multiple indicators of sleep disruption and fatigue, and most have utilized suboptimal statistical strategies. The present project identified sleep and fatigue symptom cluster groups of breast cancer patients using Latent Profile Analysis (LPA) based on two indicators of objective sleep, one measure of subjective sleep quality, and five dimensions of cancer-related fatigue. Groups were then compared on sociodemographic, medical, and psychosocial characteristics. Design. Participants were 152 women with newly diagnosed stage I-III breast cancer with no prior exposure to chemotherapy who were scheduled to receive at least four cycles of anthracycline-based chemotherapy. Participants were recruited through two separate studies with identical protocols, recruitment techniques, and inclusion criteria. Across both studies data were collected prior to the initiation of chemotherapy treatment (i.e., T1), and again at the last week of the fourth cycle of chemotherapy (i.e., T2). Exploratory LPA was used to derive categorical latent variables at T1 and T2 representing groups of individuals who scored similarly on percent of the day spent asleep and percent of the night spent asleep based on actigraphy (i.e., objective sleep), the Pittsburgh Sleep Quality Index total score (i.e., subjective sleep quality), and the General fatigue, Physical fatigue, Emotional fatigue, Mental fatigue, and Vigor subscales of the Multidimensional Fatigue Symptom Inventory-Short Form (i.e., five dimensions of cancer-related fatigue). Logistic regression analyses then evaluated if sociodemographic, medical, and psychosocial characteristics at T1 significantly predicted group membership at both time points. Analyses of covariance (ANCOVAs) evaluated if groups identified at both time points had different means on psychosocial variables at T2. The psychosocial characteristics explored included depression, climacteric symptomatology, and mental, physical, and breast cancer specific health-related quality of life. Results. At T1 (N = 152) three groups were identified, and at T2 (n = 128) five groups were identified. Bivariate logistic regression analyses demonstrated that T1 values on select sociodemographic, select medical, and all psychosocial variables significantly predicted group membership at T1 and at T2. ANCOVAs identified that, after controlling for covariates, groups identified at T1 did not significantly differ on any psychosocial variables measured at T2. Conversely, after controlling for covariates, groups identified at T2 had different means on all psychosocial variables measured at T2. Conclusions. Distinct groups with unique sleep and cancer-related fatigue experiences were found among breast cancer patients prior to the initiation of chemotherapy, and again at the last week of the fourth cycle thereof. Results identify T1 sociodemographic, medical, and psychosocial variables that can be used to indicate likely group membership, and clarify which groups may be at heightened risk for poor psychosocial outcomes at T2. These results can inform the development of assessments and interventions to improve breast cancer patients’ overall experience of disease.