Human nervous system development is an intricate and protracted process that requires precise spatio-temporal transcriptional regulation. Here we generated tissue-level and single-cell transcriptomic data from up to sixteen brain regions covering prenatal and postnatal rhesus macaque development. Integrative analysis with complementary human data revealed that global intra-species (ontogenetic) and inter-species (phylogenetic) regional transcriptomic differences exhibit concerted cup-shaped patterns, with a late fetal-to-infancy (perinatal) convergence. Prenatal neocortical transcriptomic patterns revealed transient topographic gradients, whereas postnatal patterns largely reflected functional hierarchy. Genes exhibiting heterotopic and heterochronic divergence included those transiently enriched in the prenatal prefrontal cortex or linked to autism spectrum disorder and schizophrenia. Our findings shed light on transcriptomic programs underlying the evolution of human brain development and the pathogenesis of neuropsychiatric disorders.

Cellular heterogeneity in the human brain obscures the identification of robust cellular regulatory networks, which is necessary to understand the function of non-coding elements and the impact of non-coding genetic variation. Here we integrate genome-wide chromosome conformation data from purified neurons and glia with transcriptomic and enhancer profiles, to characterize the gene regulatory landscape of two major cell classes in the human brain. We then leverage cell-type-specific regulatory landscapes to gain insight into the cellular etiology of several brain disorders. We find that Alzheimer’s disease (AD)-associated epigenetic dysregulation is linked to neurons and oligodendrocytes, whereas genetic risk factors for AD highlighted microglia, suggesting that different cell types may contribute to disease risk, via different mechanisms. Moreover, integration of glutamatergic and GABAergic regulatory maps with genetic risk factors for schizophrenia (SCZ) and bipolar disorder (BD) identifies shared (parvalbumin-expressing interneurons) and distinct cellular etiologies (upper layer neurons for BD, and deeper layer projection neurons for SCZ). Collectively, these findings shed new light on cell-type-specific gene regulatory networks in brain disorders.

BACKGROUND: Gene expression profiling (GEP) is being used increasingly for risk stratification to identify women with lymph nodenegative, estrogen receptor-positive, early stage breast cancer who are most likely to benefit from adjuvant chemotherapy. The authors of this report evaluated the cost effectiveness of recurrence score-guided treatment using 2 commercially available GEP tests, Oncotype DX (Genomic Health, Redwood City, Calif) and MammaPrint (Agendia Inc., Irvine, Calif), from a third-party payer's perspective. METHODS: A 10-year Markov model was developed to compare the costs and quality-adjusted life-years (QALYs) of treatment decisions guided by either Oncotype DX or MammaPrint in a hypothetical cohort of women with early stage, lymph nodenegative, estrogen receptor-positive breast cancer who may experience recurrence. Outcomes included no recurrence, recurrence, and death. The costs considered included gene test costs, the costs of adjuvant chemotherapy and other chemotherapy (including premedication, oncology visits, and monitoring for adverse events), the cost of treating recurrence, costs associated with the treatment of adverse events, and end-of-life care costs. RESULTS: The model demonstrated that the patients who received the Oncotype DX test to guide treatment spent $27,882 (in US dollars) and gained 7.364 QALYs, whereas patients who received the MammaPrint test to guide treatment spent $21,598 and gained 7.461 QALYs. Sensitivity analyses demonstrated that the results were robust to changes in all parameters. CONCLUSIONS: The model suggested that MammaPrint is a more cost-effective GEP test compared with Oncotype DX at a threshold willingness-to-pay of $50,000 per QALY. Because Oncotype DX is the most frequently used GEP in clinical practice in the United States, the authors concluded that the current findings have implications for health policy, particularly health insurance reimbursement decisions.