AI and immunology.

AI is rapidly becoming part of many aspects of daily life, with an impact that reaches all fields of research. We asked investigators to share their thoughts on how AI is changing immunology research, what is necessary to move forward, the potential and the pitfalls, and what will remain unchanged as the field journeys into a new era.

3D genomic mapping reveals multifocality of human pancreatic precancers.

Pancreatic intraepithelial neoplasias (PanINs) are the most common precursors of pancreatic cancer, but their small size and inaccessibility in humans make them challenging to study1. Critically, the number, dimensions and connectivity of human PanINs remain largely unknown, precluding important insights into early cancer development. Here, we provide a microanatomical survey of human PanINs by analysing 46 large samples of grossly normal human pancreas with a machine-learning pipeline for quantitative 3D histological reconstruction at single-cell resolution. To elucidate genetic relationships between and within PanINs, we developed a workflow in which 3D modelling guides multi-region microdissection and targeted and whole-exome sequencing. From these samples, we calculated a mean burden of 13 PanINs per cm3 and extrapolated that the normal intact adult pancreas harbours hundreds of PanINs, almost all with oncogenic KRAS hotspot mutations. We found that most PanINs originate as independent clones with distinct somatic mutation profiles. Some spatially continuous PanINs were found to contain multiple KRAS mutations; computational and in situ analyses demonstrated that different KRAS mutations localize to distinct cell subpopulations within these neoplasms, indicating their polyclonal origins. The extensive multifocality and genetic heterogeneity of PanINs raises important questions about mechanisms that drive precancer initiation and confer differential progression risk in the human pancreas. This detailed 3D genomic mapping of molecular alterations in human PanINs provides an empirical foundation for early detection and rational interception of pancreatic cancer.

CODA: quantitative 3D reconstruction of large tissues at cellular resolution.

A central challenge in biology is obtaining high-content, high-resolution information while analyzing tissue samples at volumes relevant to disease progression. We address this here with CODA, a method to reconstruct exceptionally large (up to multicentimeter cubed) tissues at subcellular resolution using serially sectioned hematoxylin and eosin-stained tissue sections. Here we demonstrate CODA's ability to reconstruct three-dimensional (3D) distinct microanatomical structures in pancreas, skin, lung and liver tissues. CODA allows creation of readily quantifiable tissue volumes amenable to biological research. As a testbed, we assess the microanatomy of the human pancreas during tumorigenesis within the branching pancreatic ductal system, labeling ten distinct structures to examine heterogeneity and structural transformation during neoplastic progression. We show that pancreatic precancerous lesions develop into distinct 3D morphological phenotypes and that pancreatic cancer tends to spread far from the bulk tumor along collagen fibers that are highly aligned to the 3D curves of ductal, lobular, vascular and neural structures. Thus, CODA establishes a means to transform broadly the structural study of human diseases through exploration of exhaustively labeled 3D microarchitecture.

Skin keratinocyte-derived SIRT1 and BDNF modulate mechanical allodynia in mouse models of diabetic neuropathy.

Diabetic neuropathy is a debilitating disorder characterized by spontaneous and mechanical allodynia. The role of skin mechanoreceptors in the development of mechanical allodynia is unclear. We discovered that mice with diabetic neuropathy had decreased sirtuin 1 (SIRT1) deacetylase activity in foot skin, leading to reduced expression of brain-derived neurotrophic factor (BDNF) and subsequent loss of innervation in Meissner corpuscles, a mechanoreceptor expressing the BDNF receptor TrkB. When SIRT1 was depleted from skin, the mechanical allodynia worsened in diabetic neuropathy mice, likely due to retrograde degeneration of the Meissner-corpuscle innervating Aß axons and aberrant formation of Meissner corpuscles which may have increased the mechanosensitivity. The same phenomenon was also noted in skin-keratinocyte specific BDNF knockout mice. Furthermore, overexpression of SIRT1 in skin induced Meissner corpuscle reinnervation and regeneration, resulting in significant improvement of diabetic mechanical allodynia. Overall, the findings suggested that skin-derived SIRT1 and BDNF function in the same pathway in skin sensory apparatus regeneration and highlighted the potential of developing topical SIRT1-activating compounds as a novel treatment for diabetic mechanical allodynia.

Intraparenchymal metastases as a cause for local recurrence of pancreatic cancer.

Using CODA, a technique for three-dimensional reconstruction of large tissues, Kiemen et al. report observation of a microscopic focus of pancreatic cancer found in the vasculature of grossly normal human pancreas tissue resected adjacent to a large tumour. They use TP53 and SMAD4 staining to relate the small focus to the primary tumour. This report describes a represents a probable case of intraparenchymal metastasis of pancreatic cancer, revealing a probable cause of local recurrence.

Engineered bispecific antibodies targeting the interleukin-6 and -8 receptors potently inhibit cancer cell migration and tumor metastasis.

Simultaneous inhibition of interleukin-6 (IL-6) and interleukin-8 (IL-8) signaling diminishes cancer cell migration, and combination therapy has recently been shown to synergistically reduce metastatic burden in a preclinical model of triple-negative breast cancer. Here, we have engineered two novel bispecific antibodies that target the IL-6 and IL-8 receptors to concurrently block the signaling activity of both ligands. We demonstrate that a first-in-class bispecific antibody design has promising therapeutic potential, with enhanced selectivity and potency compared with monoclonal antibody and small-molecule drug combinations in both cellular and animal models of metastatic triple-negative breast cancer. Mechanistic characterization revealed that our engineered bispecific antibodies have no impact on cell viability, but profoundly reduce the migratory potential of cancer cells; hence they constitute a true anti-metastatic treatment. Moreover, we demonstrate that our antibodies can be readily combined with standard-of-care anti-proliferative drugs to develop effective anti-cancer regimens. Collectively, our work establishes an innovative metastasis-focused direction for cancer drug development.

Characterization of tumor-associated macrophages in prostate cancer transgenic mouse models.

BACKGROUND: Tumor-associated macrophages (TAMs) are critical components of the tumor microenvironment (TME) in prostate cancer. Commonly used orthotopic models do not accurately reflect the complete TME of a human patient or the natural initiation and progression of a tumor. Therefore, genetically engineered mouse models are essential for studying the TME as well as advancing TAM-targeted therapies. Two common transgenic (TG) models of prostate cancer are Hi-Myc and transgenic adenocarcinoma of the mouse prostate (TRAMP), but the TME and TAM characteristics of these models have not been well characterized. METHODS: To advance the Hi-Myc and TRAMP models as tools for TAM studies, macrophage infiltration and characteristics were assessed using histopathologic, flow cytometric, and expression analyses in these models at various timepoints during tumor development and progression. RESULTS: In both Hi-Myc and TRAMP models, macrophages adopt a more pro-tumor phenotype in higher histological grade tumors and in older prostate tissue. However, the Hi-Myc and TRAMP prostates differ in their macrophage density, with Hi-Myc tumors exhibiting increased macrophage density and TRAMP tumors exhibiting decreased macrophage density compared to age-matched wild type mice. CONCLUSIONS: The macrophage density and the adenocarcinoma cancer subtype of Hi-Myc appear to better mirror patient tumors, suggesting that the Hi-Myc model is the more appropriate in vivo TG model for studying TAMs and TME-targeted therapies.

Three-dimensional analysis of extrahepatic cholangiocarcinoma and tumor budding.

Advances in tissue clearing and microscopy make it possible to study human diseases in three dimensions (3D). High-grade tumor budding is known to be associated with poor prognosis in various cancers; however, little is known about the 3D architecture of tumor budding. Using tissue clearing, we analyzed the 3D structure of tumor budding and E-cadherin expression in 31 extrahepatic cholangiocarcinomas. A total of 31 thick slabs (up to 5 mm) were harvested from surgically resected tumor tissue, including 27 hilar and 4 distal cholangiocarcinomas. Twenty-eight cases were adenocarcinoma, and three were undifferentiated carcinoma. After clearing, the tissues were immunolabeled with antibodies to cytokeratin 19 and to E-cadherin, and then visualized using light-sheet and confocal laser scanning microscopy. Tumor budding was evaluated in hematoxylin and eosin-stained sections (2D) using standard pathological criteria. Of the 31 cancers, 13 showed low-grade tumor budding and 18 showed high-grade tumor budding. First, 3D analysis revealed that the neoplastic cells in tumor buds of adenocarcinoma were typically not individual islands of cells, but rather tips of attenuated protrusions connected to the main tumor. Second, adenocarcinomas with low-grade tumor budding were composed predominantly of tubules that only focally form cords at the periphery. By contrast, adenocarcinomas with high-grade tumor budding predominantly formed cords in both centers and peripheries of the tumors. Third, adenocarcinoma with low-grade tumor budding was characterized by a few short protrusions with few branches, whereas adenocarcinoma with high-grade tumor budding was characterized by longer protrusions with more branching. Finally, immunolabeling of E-cadherin was stronger in the center of the adenocarcinoma but decreased at the tips of protrusions. E-cadherin loss was more extensive in the protrusions of high-grade tumor budding than in the protrusions of low-grade tumor budding. Our findings suggest that tumor buds as seen in 2D are, in fact, cross-sections of attenuated but contiguous protrusions extending from the main tumor. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Three-dimensional visualization of cleared human pancreas cancer reveals that sustained epithelial-to-mesenchymal transition is not required for venous invasion.

Venous invasion is three times more common in pancreatic cancer than it is in other major cancers of the gastrointestinal tract, and venous invasion may explain why pancreatic cancer is so deadly. To characterize the patterns of venous invasion in pancreatic cancer, 52 thick slabs (up to 5 mm) of tissue were harvested from 52 surgically resected human ductal adenocarcinomas, cleared with a modified iDISCO method, and labeled with fluorescent-conjugated antibodies to cytokeratin 19, desmin, CD31, p53 and/or e-cadherin. Labeled three-dimensional (3D) pancreas cancer tissues were visualized with confocal laser scanning or light sheet microscopy. Multiple foci of venous and even arterial invasion were visualized. Venous invasion was detected more often in 3D (88%, 30/34 cases) than in conventional 2D slide evaluation (75%, 25/34 cases, P < 0.001). 3D visualization revealed pancreatic cancer cells crossing the walls of veins at multiple points, often at points where preexisting capillary structures bridge the blood vessels. The neoplastic cells often retained a ductal morphology (cohesive cells forming tubes) as they progressed from a stromal to intravenous location. Although immunolabeling with antibodies to e-cadherin revealed focal loss of expression at the leading edges of the cancers, the neoplastic cells within veins expressed e-cadherin and formed well-oriented glands. We conclude that venous invasion is almost universal in pancreatic cancer, suggesting that even surgically resectable PDAC has access to the venous spaces and thus the ability to disseminate widely. Furthermore, we observe that sustained epithelial-mesenchymal transition is not required for venous invasion in pancreatic cancer.

Search for the Source of an Apparent Interfacial Resistance To Mass Transfer of CnEm Surfactants To the Water/Oil Interface.

Experiments have shown that relaxation of oil/water interfacial tension by adsorption of alkyl ethoxylate surfactants from water onto an oil droplet is delayed relative to diffusion-controlled adsorption. We examine possible causes of this delay, and we show that several are implausible. We find that redissolution of the surfactant in the oil droplet cannot explain the apparent interfacial resistance at short times because the interface will preferentially fill before any such redissolution occurs. We also perform umbrella sampling with molecular dynamics simulation and do not find any evidence of a free-energy barrier or low-diffusivity zone near the interface. Nor do we find evidence from the simulation that premicellar aggregation slows diffusion enough to cause the observed resistance to interfacial adsorption. We are therefore unable to pinpoint the cause of the resistance, but we suggest that "dead time" associated with the experimental method could be responsible-specifically a local depletion of surfactant by the ejected droplet when creating the fresh interface between the oil and water.

Three-dimensional reconstruction of fetal rhesus macaque kidneys at single-cell resolution reveals complex inter-relation of structures.

Kidneys are among the most structurally complex organs in the body. Their architecture is critical to ensure proper function and is often impacted by diseases such as diabetes and hypertension. Understanding the spatial interplay between the different structures of the nephron and renal vasculature is crucial. Recent efforts have demonstrated the value of three-dimensional (3D) imaging in revealing new insights into the various components of the kidney; however, these studies used antibodies or autofluorescence to detect structures and so were limited in their ability to compare the many subtle structures of the kidney at once. Here, through 3D reconstruction of fetal rhesus macaque kidneys at cellular resolution, we demonstrate the power of deep learning in exhaustively labelling seventeen microstructures of the kidney. Using these tissue maps, we interrogate the spatial distribution and spatial correlation of the glomeruli, renal arteries, and the nephron. This work demonstrates the power of deep learning applied to 3D tissue images to improve our ability to compare many microanatomical structures at once, paving the way for further works investigating renal pathologies.

Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings.

Pancreatic ductal adenocarcinoma is a rare but lethal cancer. Recent evidence suggests that pancreatic intraepithelial neoplasia (PanIN), a microscopic precursor lesion that gives rise to pancreatic cancer, is larger and more prevalent than previously believed. Better understanding of the growth-law dynamics of PanINs may improve our ability to understand how a miniscule fraction makes the transition to invasive cancer. Here, using three-dimensional tissue mapping, we analyzed >1000 PanINs and found that lesion size is distributed according to a power law. Our data suggest that in bulk, PanIN size can be predicted by general growth behavior without consideration for the heterogeneity of the pancreatic microenvironment or an individual's age, history, or lifestyle. Our models suggest that intraductal spread and fusing of lesions drive our observed size distribution. This analysis lays the groundwork for future mathematical modeling efforts integrating PanIN incidence, morphology, and molecular features to understand tumorigenesis and demonstrates the utility of combining experimental measurement with dynamic modeling in understanding tumorigenesis.

Skin keratinocyte-derived SIRT1 and BDNF modulate mechanical allodynia in mouse models of diabetic neuropathy.

Diabetic neuropathy is a debilitating disorder characterized by spontaneous and mechanical pain. The role of skin mechanoreceptors in the development of mechanical pain (allodynia) is unclear. We discovered that mice with diabetic neuropathy had decreased sirtuin 1 (SIRT1) deacetylase activity in foot skin, leading to reduced expression of brain-derived neurotrophic factor (BDNF) and subsequent loss of innervation in Meissner corpuscles, a mechanoreceptor expressing the BDNF receptor TrkB. When SIRT1 was depleted from skin, the mechanical allodynia worsened in diabetic neuropathy mice, likely due to retrograde degeneration of the Meissner-corpuscle innervating Aß axons and aberrant formation of Meissner corpuscles which may have increased the mechanosensitivity. The same phenomenon was also noted in skin BDNF knockout mice. Furthermore, overexpression of SIRT1 in skin induced Meissner corpuscle reinnervation and regeneration, resulting in significant improvement of diabetic mechanical allodynia. Overall, the findings suggested that skin-derived SIRT1 and BDNF function in the same pathway in skin sensory apparatus regeneration and highlighted the potential of developing topical SIRT1-activating compounds as a novel treatment for diabetic mechanical allodynia.

PanIN or IPMN? Redefining Lesion Size in 3 Dimensions.

Pancreatic ductal adenocarcinoma (PDAC) develops from 2 known precursor lesions: a majority (∼85%) develops from pancreatic intraepithelial neoplasia (PanIN), and a minority develops from intraductal papillary mucinous neoplasms (IPMNs). Clinical classification of PanIN and IPMN relies on a combination of low-resolution, 3-dimensional (D) imaging (computed tomography, CT), and high-resolution, 2D imaging (histology). The definitions of PanIN and IPMN currently rely heavily on size. IPMNs are defined as macroscopic: generally >1.0 cm and visible in CT, and PanINs are defined as microscopic: generally <0.5 cm and not identifiable in CT. As 2D evaluation fails to take into account 3D structures, we hypothesized that this classification would fail in evaluation of high-resolution, 3D images. To characterize the size and prevalence of PanINs in 3D, 47 thick slabs of pancreas were harvested from grossly normal areas of pancreatic resections, excluding samples from individuals with a diagnosis of an IPMN. All patients but one underwent preoperative CT scans. Through construction of cellular resolution 3D maps, we identified >1400 ductal precursor lesions that met the 2D histologic size criteria of PanINs. We show that, when 3D space is considered, 25 of these lesions can be digitally sectioned to meet the 2D histologic size criterion of IPMN. Re-evaluation of the preoperative CT images of individuals found to possess these large precursor lesions showed that nearly half are visible on imaging. These findings demonstrate that the clinical classification of PanIN and IPMN fails in evaluation of high-resolution, 3D images, emphasizing the need for re-evaluation of classification guidelines that place significant weight on 2D assessment of 3D structures.

Three-dimensional assessments are necessary to determine the true, spatially-resolved composition of tissues.

Methods for spatially resolved cellular profiling using thinly cut sections have enabled in-depth quantitative tissue mapping to study inter-sample and intra-sample differences in normal human anatomy and disease onset and progression. These methods often profile extremely limited regions, which may impact the evaluation of heterogeneity due to tissue sub-sampling. Here, we applied CODA, a deep learning-based tissue mapping platform, to reconstruct the three-dimensional (3D) microanatomy of grossly normal and cancer-containing human pancreas biospecimens obtained from individuals who underwent pancreatic resection. To compare inter- and intra-sample heterogeneity, we assessed bulk and spatially resolved tissue composition in a cohort of two-dimensional (2D) whole slide images (WSIs) and a cohort of thick slabs of pancreas tissue that were digitally reconstructed in 3D from serial sections. To demonstrate the marked under sampling of 2D assessments, we simulated the number of WSIs and tissue microarrays (TMAs) necessary to represent the compositional heterogeneity of 3D data within 10% error to reveal that tens of WSIs and hundreds of TMA cores are sometimes needed. We show that spatial correlation of different pancreatic structures decay significantly within a span of microns, demonstrating that 2D histological sections may not be representative of their neighboring tissues. In sum, we demonstrate that 3D assessments are necessary to accurately assess tissue composition in normal and abnormal specimens and in order to accurately determine neoplastic content. These results emphasize the importance of intra-sample heterogeneity in tissue mapping efforts.

Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping.

The development of novel imaging platforms has improved our ability to collect and analyze large three-dimensional (3D) biological imaging datasets. Advances in computing have led to an ability to extract complex spatial information from these data, such as the composition, morphology, and interactions of multi-cellular structures, rare events, and integration of multi-modal features combining anatomical, molecular, and transcriptomic (among other) information. Yet, the accuracy of these quantitative results is intrinsically limited by the quality of the input images, which can contain missing or damaged regions, or can be of poor resolution due to mechanical, temporal, or financial constraints. In applications ranging from intact imaging (e.g. light-sheet microscopy and magnetic resonance imaging) to sectioning based platforms (e.g. serial histology and serial section transmission electron microscopy), the quality and resolution of imaging data has become paramount. Here, we address these challenges by leveraging frame interpolation for large image motion (FILM), a generative AI model originally developed for temporal interpolation, for spatial interpolation of a range of 3D image types. Comparative analysis demonstrates the superiority of FILM over traditional linear interpolation to produce functional synthetic images, due to its ability to better preserve biological information including microanatomical features and cell counts, as well as image quality, such as contrast, variance, and luminance. FILM repairs tissue damages in images and reduces stitching artifacts. We show that FILM can decrease imaging time by synthesizing skipped images. We demonstrate the versatility of our method with a wide range of imaging modalities (histology, tissue-clearing/light-sheet microscopy, magnetic resonance imaging, serial section transmission electron microscopy), species (human, mouse), healthy and diseased tissues (pancreas, lung, brain), staining techniques (IHC, H&E), and pixel resolutions (8 nm, 2 µm, 1mm). Overall, we demonstrate the potential of generative AI in improving the resolution, throughput, and quality of biological image datasets, enabling improved 3D imaging.

3D histology reveals that immune response to pancreatic precancers is heterogeneous and depends on global pancreas structure.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer for which few effective therapies exist. Immunotherapies specifically are ineffective in pancreatic cancer, in part due to its unique stromal and immune microenvironment. Pancreatic intraepithelial neoplasia, or PanIN, is the main precursor lesion to PDAC. Recently it was discovered that PanINs are remarkably abundant in the grossly normal pancreas, suggesting that the vast majority will never progress to cancer. Here, through construction of 48 samples of cm3-sized human pancreas tissue, we profiled the immune microenvironment of 1,476 PanINs in 3D and at single-cell resolution to better understand the early evolution of the pancreatic tumor microenvironment and to determine how inflammation may play a role in cancer progression. We found that bulk pancreatic inflammation strongly correlates to PanIN cell fraction. We found that the immune response around PanINs is highly heterogeneous, with distinct immune hotspots and cold spots that appear and disappear in a span of tens of microns. Immune hotspots generally mark locations of higher grade of dysplasia or locations near acinar atrophy. The immune composition at these hotspots is dominated by naïve, cytotoxic, and regulatory T cells, cancer associated fibroblasts, and tumor associated macrophages, with little similarity to the immune composition around less-inflamed PanINs. By mapping FOXP3+ cells in 3D, we found that regulatory T cells are present at higher density in larger PanIN lesions compared to smaller PanINs, suggesting that the early initiation of PanINs may not exhibit an immunosuppressive response. This analysis demonstrates that while PanINs are common in the pancreases of most individuals, inflammation may play a pivotal role, both at the bulk and the microscopic scale, in demarcating regions of significance in cancer progression.

qMAP enabled microanatomical mapping of human skin aging.

Aging is a major driver of diseases in humans. Identifying features associated with aging is essential for designing robust intervention strategies and discovering novel biomarkers of aging. Extensive studies at both the molecular and organ/whole-body physiological scales have helped determined features associated with aging. However, the lack of meso-scale studies, particularly at the tissue level, limits the ability to translate findings made at molecular scale to impaired tissue functions associated with aging. In this work, we established a tissue image analysis workflow - quantitative micro-anatomical phenotyping (qMAP) - that leverages deep learning and machine vision to fully label tissue and cellular compartments in tissue sections. The fully mapped tissue images address the challenges of finding an interpretable feature set to quantitatively profile age-related microanatomic changes. We optimized qMAP for skin tissues and applied it to a cohort of 99 donors aged 14 to 92. We extracted 914 microanatomic features and found that a broad spectrum of these features, represented by 10 cores processes, are strongly associated with aging. Our analysis shows that microanatomical features of the skin can predict aging with a mean absolute error (MAE) of 7.7 years, comparable to state-of-the-art epigenetic clocks. Our study demonstrates that tissue-level architectural changes are strongly associated with aging and represent a novel category of aging biomarkers that complement molecular markers. Our results highlight the complex and underexplored multi-scale relationship between molecular and tissue microanatomic scales.

High-motility pro-tumorigenic monocytes drive macrophage enrichment in the tumor microenvironment.

Enrichment of tumor-associated macrophages (TAMΦs) in the tumor microenvironment correlates with worse clinical outcomes in triple-negative breast cancer (TNBC) patients, prompting the development of therapies to inhibit TAMΦ infiltration. However, the lackluster efficacy of CCL2-based chemotaxis blockade in clinical trials suggests that a new understanding of monocyte/macrophage infiltration may be necessary. Here we demonstrate that random migration, and not only chemotaxis, drives macrophage tumor infiltration. We identified tumor- associated monocytes (TAMos) that display a dramatically enhanced migration capability, induced rapidly by the tumor microenvironment, that drives effective tumor infiltration, in contrast to low-motility differentiated macrophages. TAMo, not TAMΦ, promotes cancer cell proliferation through activation of the MAPK pathway. IL-6 secreted both by cancer cells and TAMo themselves enhances TAMo migration by increasing dendritic protrusion dynamics and myosin- based contractility via the JAK2/STAT3 signaling pathway. Independent from CCL2 mediated chemotaxis, IL-6 driven enhanced migration and pro-proliferative effect of TAMo were validated in a syngeneic TNBC mouse model. Depletion of IL-6 in cancer cells significantly attenuated monocyte infiltration and reversed TAMo-induced cancer cell proliferation. This work reveals the critical role random migration plays in monocyte driven TAMΦ enrichment in a tumor and pinpoints IL-6 as a potential therapeutic target in combination with CCL2 to ameliorate current strategies against TAMΦ infiltration.

Engineering self-propelled tumor-infiltrating CAR T cells using synthetic velocity receptors.

Chimeric antigen receptor (CAR) T cells express antigen-specific synthetic receptors, which upon binding to cancer cells, elicit T cell anti-tumor responses. CAR T cell therapy has enjoyed success in the clinic for hematological cancer indications, giving rise to decade-long remissions in some cases. However, CAR T therapy for patients with solid tumors has not seen similar success. Solid tumors constitute 90% of adult human cancers, representing an enormous unmet clinical need. Current approaches do not solve the central problem of limited ability of therapeutic cells to migrate through the stromal matrix. We discover that T cells at low and high density display low- and high-migration phenotypes, respectively. The highly migratory phenotype is mediated by a paracrine pathway from a group of self-produced cytokines that include IL5, TNFα, IFNγ, and IL8. We exploit this finding to "lock-in" a highly migratory phenotype by developing and expressing receptors, which we call velocity receptors (VRs). VRs target these cytokines and signal through these cytokines' cognate receptors to increase T cell motility and infiltrate lung, ovarian, and pancreatic tumors in large numbers and at doses for which control CAR T cells remain confined to the tumor periphery. In contrast to CAR therapy alone, VR-CAR T cells significantly attenuate tumor growth and extend overall survival. This work suggests that approaches to the design of immune cell receptors that focus on migration signaling will help current and future CAR cellular therapies to infiltrate deep into solid tumors.