Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous largescale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.Cancer forms and progresses through a series of critical transitions-from pre-malignant to malignant states, from locally contained to metastatic disease, and from treatment-responsive to treatment-resistant tumors (Figure 1). Although specifics differ across tumor types and patients, all transitions involve complex dynamic interactions between diverse pre-malignant, malignant, and non-malignant cells (e.g., stroma cells and immune cells), often organized in specific patterns within the tumor

The Rab GTPases are the largest family of proteins regulating membrane traffic. Rab proteins form a nidus for the assembly of multiprotein complexes on distinct vesicle membranes to regulate particular membrane trafficking pathways. Recent investigations have demonstrated that Myosin Vb (Myo5B) is an effector for Rab8a, Rab10, and Rab11a, all of which are implicated in regulating different pathways for recycling of proteins to the plasma membrane. It remains unclear how specific interactions of Myo5B with individual Rab proteins can lead to specificity in the regulation of alternate trafficking pathways. We examined the relative contributions of Rab/Myo5B interactions with specific pathways using Myo5B mutants lacking binding to either Rab11a or Rab8a. Myo5B Q1300L and Y1307C mutations abolished Rab8a association, whereas Myo5B Y1714E and Q1748R mutations uncoupled association with Rab11a. Expression of Myo5B tails containing these mutants demonstrated that Rab11a, but not Rab8a, was required for recycling of transferrin in nonpolarized cells. In contrast, in polarized epithelial cyst cultures, Myo5B was required for apical membrane trafficking and de novo lumen formation, dependent on association with both Rab8a and Rab11a. These data demonstrate that different combinations of Rab GTPase association with Myo5B control distinct membrane trafficking pathways.

Colorectal cancers (CRCs) arise from precursor polyps whose cellular origins, molecular heterogeneity, and immunogenic potential may reveal diagnostic and therapeutic insights when analyzed at high resolution. We present a single-cell transcriptomic and imaging atlas of the two most common human colorectal polyps, conventional adenomas and serrated polyps, and their resulting CRC counterparts. Integrative analysis of 128 datasets from 62 participants reveals adenomas arise from WNT-driven expansion of stem cells, while serrated polyps derive from differentiated cells through gastric metaplasia. Metaplasia-associated damage is coupled to a cytotoxic immune microenvironment preceding hypermutation, driven partly by ll

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Summary Modern single-cell technologies allow multiplexed sampling of cellular states within a tissue. However, computational tools that can infer developmental cell-state transitions reproducibly from such single-cell data are lacking. Here, we introduce p-Creode, an unsupervised algorithm that produces multi-branching graphs from single-cell data, compares graphs with differing topologies, and infers a statistically robust hierarchy of cell-state transitions that define developmental trajectories. We have applied p-Creode to mass cytometry, multiplex immunofluorescence, and single-cell RNA-seq data. As a test case, we validate cell state-transition trajectories predicted by p-Creode for intestinal tuft cells, a rare, chemosensory cell type. We clarify that tuft cells are specified outside of the Atoh1-dependent secretory lineage in the small intestine. However, p-Creode also predicts, and we confirm, that tuft cells arise from an alternative, Atoh1-driven developmental program in the colon. These studies introduce p-Creode as a reliable method for analyzing large datasets that depict branching transition trajectories. p-Creode is publicly available for download here: https://github.com/KenLauLab/pCreode.