Spatially resolved analysis of pancreatic cancer identifies therapy-associated remodeling of the tumor microenvironment.

In combination with cell-intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with neoadjuvant chemotherapy and radiotherapy. We developed spatially constrained optimal transport interaction analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression. Our results uncovered a marked change in ligand-receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid coculture system. We identified enrichment in interleukin-6 family signaling that functionally confers resistance to chemotherapy. Overall, this study demonstrates that characterization of the tumor microenvironment using single-cell spatial transcriptomics allows for the identification of molecular interactions that may play a role in the emergence of therapeutic resistance and offers a spatially based analysis framework that can be broadly applied to other contexts.

Therapy-associated remodeling of pancreatic cancer revealed by single-cell spatial transcriptomics and optimal transport analysis.

In combination with cell intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged high-plex single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with specific malignant subtypes and neoadjuvant chemotherapy/radiotherapy. We developed Spatially Constrained Optimal Transport Interaction Analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression. Our results uncovered a marked change in ligand-receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid co-culture system. Overall, this study demonstrates that characterization of the tumor microenvironment using high-plex single-cell spatial transcriptomics allows for identification of molecular interactions that may play a role in the emergence of chemoresistance and establishes a translational spatial biology paradigm that can be broadly applied to other malignancies, diseases, and treatments.

Precise DNA cleavage using CRISPR-SpRYgests.

Methods for in vitro DNA cleavage and molecular cloning remain unable to precisely cleave DNA directly adjacent to bases of interest. Restriction enzymes (REs) must bind specific motifs, whereas wild-type CRISPR-Cas9 or CRISPR-Cas12 nucleases require protospacer adjacent motifs (PAMs). Here we explore the utility of our previously reported near-PAMless SpCas9 variant, named SpRY, to serve as a universal DNA cleavage tool for various cloning applications. By performing SpRY DNA digests (SpRYgests) using more than 130 guide RNAs (gRNAs) sampling a wide diversity of PAMs, we discovered that SpRY is PAMless in vitro and can cleave DNA at practically any sequence, including sites refractory to cleavage with wild-type SpCas9. We illustrate the versatility and effectiveness of SpRYgests to improve the precision of several cloning workflows, including those not possible with REs or canonical CRISPR nucleases. We also optimize a rapid and simple one-pot gRNA synthesis protocol to streamline SpRYgest implementation. Together, SpRYgests can improve various DNA engineering applications that benefit from precise DNA breaks.

Treatment-associated remodeling of the pancreatic cancer endothelium at single-cell resolution.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most treatment refractory and lethal malignancies. The diversity of endothelial cell (EC) lineages in the tumor microenvironment (TME) impacts the efficacy of antineoplastic therapies, which in turn remodel EC states and distributions. Here, we present a single-cell resolution framework of diverse EC lineages in the PDAC TME in the context of neoadjuvant chemotherapy, radiotherapy, and losartan. We analyzed a custom single-nucleus RNA-seq dataset derived from 37 primary PDAC specimens (18 untreated, 14 neoadjuvant FOLFIRINOX + chemoradiotherapy, 5 neoadjuvant FOLFIRINOX + chemoradiotherapy + losartan). A single-nucleus transcriptome analysis of 15,185 EC profiles revealed two state programs (ribosomal, cycling), four lineage programs (capillary, arterial, venous, lymphatic), and one program that did not overlap significantly with prior signatures but was enriched in pathways involved in vasculogenesis, stem-like state, response to wounding and hypoxia, and endothelial-to-mesenchymal transition (reactive EndMT). A bulk transcriptome analysis of two independent cohorts (n = 269 patients) revealed that the lymphatic and reactive EndMT lineage programs were significantly associated with poor clinical outcomes. While losartan and proton therapy were associated with reduced lymphatic ECs, these therapies also correlated with an increase in reactive EndMT. Thus, the development and inclusion of EndMT-inhibiting drugs (e.g., nintedanib) to a neoadjuvant chemoradiotherapy regimen featuring losartan and/or proton therapy may be most effective in depleting both lymphatic and reactive EndMT populations and potentially improving patient outcomes.

Genomic alterations predictive of poor clinical outcomes in pan-cancer.

BACKGROUND: Genomic alterations are highly frequent across cancers, but their prognostic impact is not well characterized in pan-cancer cohorts. Here, we use pan-cancer cohorts from TCGA and MSK-IMPACT to evaluate the associations of common genomic alterations with poor clinical outcome. MATERIALS AND METHODS: Genomic alterations in commonly altered genes were extracted from Pan-Cancer TCGA and MSK-IMPACT cohorts. Multivariable Cox regression analyses stratified by cancer type defined adjusted hazard ratios (AHRs) for disease-specific survival (DSS), progression-free survival (PFS) and overall survival (OS). RESULTS: Using TCGA we identified 32 mutated genes, and 15 copy number (CN) genes with frequency >= 4% in 9,104 patients across 28 cancers. On UVA, having a TP53-mutations or any mutation in the 31 genes (mut31) were associated with worse PFS (HR: 1.22, p < 0.0001 and HR: 1.1, p = 0.04, respectively) and DSS (HR: 1.38, p < 0.0001, and HR: 1.16, p = 0.03, respectively). CDKN2A, PTEN deletions, and MYC-amplifications were associated with PFS and DSS (p < 0.05 for all). On MVA, including TP53-mutations, mut31, CDKN2A-deletion, PTEN-deletion, and MYC-amplification, all five alterations were independently prognostic of poor PFS and DSS. Similar results were observed in an independent cohort from MSK-IMPACT (n = 7,051) where TP53 was associated with poor OS independent of mut31 and CN alterations in CDKN2A, PTEN, and MYC in primary tumors (p < 0.0001). CONCLUSIONS: TP53-mutations, CDKN2A-deletion, PTEN-deletion, and MYC-amplification are independent pan-cancer prognostic genomic alterations.

Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal and treatment-refractory cancer. Molecular stratification in pancreatic cancer remains rudimentary and does not yet inform clinical management or therapeutic development. Here, we construct a high-resolution molecular landscape of the cellular subtypes and spatial communities that compose PDAC using single-nucleus RNA sequencing and whole-transcriptome digital spatial profiling (DSP) of 43 primary PDAC tumor specimens that either received neoadjuvant therapy or were treatment naive. We uncovered recurrent expression programs across malignant cells and fibroblasts, including a newly identified neural-like progenitor malignant cell program that was enriched after chemotherapy and radiotherapy and associated with poor prognosis in independent cohorts. Integrating spatial and cellular profiles revealed three multicellular communities with distinct contributions from malignant, fibroblast and immune subtypes: classical, squamoid-basaloid and treatment enriched. Our refined molecular and cellular taxonomy can provide a framework for stratification in clinical trials and serve as a roadmap for therapeutic targeting of specific cellular phenotypes and multicellular interactions.

DNA repair and immune checkpoint blockade response.

Immune checkpoint blockade (ICB) has shown immense promise for treating patients with various cancer types, but its effectiveness relies on our ability to identify likely responders. Here, we examined the association between mutations in 25 core DNA repair genes and ICB outcomes in 6619 patients across 9 cancer types with advanced disease and MSK-IMPACT tumor sequencing. Notably, we observed that mutations in 7 of the DNA repair genes (ATM, ATR, POLE, ERCC4, NBN, RAD50, PARP1) were significantly associated with improved overall survival in ICB-treated patients (p < 0.05 for all) and had significant interaction with treatment (pinteraction <0.05 for all). Similarly, DNA repair mutations were enriched in other cancer types not previously assessed and primary tumors of unknown origins, suggesting that mutations could serve as a biomarker independent of cancer type. Although our cohort was enriched in certain cancer types, such as melanoma and non-small cell lung cancer, and clinically matched samples were not assessed, our study provides a robust approach in characterizing clinically-adoptable biomarkers that can select for potential ICB responders.

Therapeutic avenues for cancer neuroscience: translational frontiers and clinical opportunities.

With increasing attention on the essential roles of the tumour microenvironment in recent years, the nervous system has emerged as a novel and crucial facilitator of cancer growth. In this Review, we describe the foundational, translational, and clinical advances illustrating how nerves contribute to tumour proliferation, stress adaptation, immunomodulation, metastasis, electrical hyperactivity and seizures, and neuropathic pain. Collectively, this expanding knowledge base reveals multiple therapeutic avenues for cancer neuroscience that warrant further exploration in clinical studies. We discuss the available clinical data, including ongoing trials investigating novel agents targeting the tumour-nerve axis, and the therapeutic potential for repurposing existing neuroactive drugs as an anti-cancer approach, particularly in combination with established treatment regimens. Lastly, we discuss the clinical challenges of these treatment strategies and highlight unanswered questions and future directions in the burgeoning field of cancer neuroscience.

Novel genomic signature predictive of response to immune checkpoint blockade: A pan-cancer analysis from project Genomics Evidence Neo-plasia Information Exchange (GENIE).

BACKGROUND: High tumor mutation burden (TMB) and total mutation count (TMC) can be predictive of better response to immune checkpoint blockade (ICB). Nevertheless, TMB and TMC are limited by variation across cancers and inconsistent definitions due to different profiling methods (targeted vs whole genome sequencing). Our objective was to identify genomic alterations (GAs) associated with ICB response and builds a novel genomic signature predictive of ICB response, independent of TMB/TMC. METHODS: This was a pan-cancer next generation sequencing (NGS)-association study using January 2014-May 2016 data from AACR Project Genomics Evidence Neo-plasia Information Exchange (GENIE). Participants included 6619 patients with metastatic or un-resectable cancer across 9 cancer types (including 1572 ICB-treated patients). GA data was collected using next-generation sequencing (NGS) assays and downloaded from cbioportal.org. Predictive analyses for ICB response were performed to develop the signature (ImmGA). RESULTS: GAs in 16 genes were associated with improved OS in ICB-treated patients (p < 0.005). 13 GAs were associated with an OS benefit in ICB-treated patients (Pinteraction < 0.05); these genes composed the ImmGA signature. High ImmGA score (≥2 alterations out of 13 predictive GAs) was associated with better OS in ICB-treated patients (AHR:0.67, 95%CI [0.6-0.75], p = 1.4e-12), even after accounting for TMC (Pinteraction = 8e-16). High ImmGA was associated with better OS in ICB-treated patients across most cancers and across different ICB treatment modalities. CONCLUSION: A novel signature predictive of ICB response (ImmGA) was developed from 13 GAs. Further investigation of the utility of ImmGA for treatment and trial selection is warranted.

Pan-Cancer Survival Classification With Clinicopathological and Targeted Gene Expression Features.

Prognostication for patients with cancer is important for clinical planning and management, but remains challenging given the large number of factors that can influence outcomes. As such, there is a need to identify features that can robustly predict patient outcomes. We evaluated 8608 patient tumor samples across 16 cancer types from The Cancer Genome Atlas and generated distinct survival classifiers for each using clinical and histopathological data accessible to standard oncology workflows. For cancers that had poor model performance, we deployed a random-forest-embedded sequential forward selection approach that began with an initial subset of the 15 most predictive clinicopathological features before sequentially appending the next most informative gene as an additional feature. With classifiers derived from clinical and histopathological features alone, we observed cancer-type-dependent model performance and an area under the receiver operating curve (AUROC) range of 0.65 to 0.91 across all 16 cancer types for 1- and 3-year survival prediction, with some classifiers consistently outperforming those for others. As such, for cancers that had poor model performance, we posited that the addition of more complex biomolecular features could enhance our ability to prognose patients where clinicopathological features were insufficient. With the inclusion of gene expression data, model performance for 3 select cancers (glioblastoma, stomach/gastric adenocarcinoma, ovarian serous carcinoma) markedly increased from initial AUROC scores of 0.66, 0.69, and 0.67 to 0.76, 0.77, and 0.77, respectively. As a whole, this study provides a thorough examination of the relative contributions of clinical, pathological, and gene expression data in predicting overall survival and reveals cancer types for which clinical features are already strong predictors and those where additional biomolecular information is needed.

Analysis of off-target effects in CRISPR-based gene drives in the human malaria mosquito.

CRISPR-Cas9 nuclease-based gene drives have been developed toward the aim of control of the human malaria vector Anopheles gambiae Gene drives are based on an active source of Cas9 nuclease in the germline that promotes super-Mendelian inheritance of the transgene by homology-directed repair ("homing"). Understanding whether CRISPR-induced off-target mutations are generated in Anopheles mosquitoes is an important aspect of risk assessment before any potential field release of this technology. We compared the frequencies and the propensity of off-target events to occur in four different gene-drive strains, including a deliberately promiscuous set-up, using a nongermline restricted promoter for SpCas9 and a guide RNA with many closely related sites (two or more mismatches) across the mosquito genome. Under this scenario we observed off-target mutations at frequencies no greater than 1.42%. We witnessed no evidence that CRISPR-induced off-target mutations were able to accumulate (or drive) in a mosquito population, despite multiple generations' exposure to the CRISPR-Cas9 nuclease construct. Furthermore, judicious design of the guide RNA used for homing of the CRISPR construct, combined with tight temporal constriction of Cas9 expression to the germline, rendered off-target mutations undetectable. The findings of this study represent an important milestone for the understanding and managing of CRISPR-Cas9 specificity in mosquitoes, and demonstrates that CRISPR off-target editing in the context of a mosquito gene drive can be reduced to minimal levels.

Refining the Molecular Framework for Pancreatic Cancer with Single-cell and Spatial Technologies.

Pancreatic ductal adenocarcinoma (PDAC) is a treatment-refractory malignancy in urgent need of a molecular framework for guiding therapeutic strategies. Bulk transcriptomic efforts over the past decade have yielded two broad consensus subtypes: classical pancreatic/epithelial versus basal-like/squamous/quasi-mesenchymal. Although this binary classification enables prognostic stratification, it does not currently inform the administration of treatments uniquely sensitive to either subtype. Furthermore, bulk mRNA studies are challenged by distinguishing contributions from the neoplastic compartment versus other cell types in the microenvironment, which is accentuated in PDAC given that neoplastic cellularity can be low. The application of single-cell transcriptomics to pancreatic tumors has generally lagged behind other cancer types due in part to the difficulty of extracting high-quality RNA from enzymatically degradative tissue, but emerging studies have and will continue to shed light on intratumoral heterogeneity, malignant-stromal interactions, and subtle transcriptional programs previously obscured at the bulk level. In conjunction with insights provided by single-cell/nucleus dissociative techniques, spatially resolved technologies should also facilitate the contextualization of gene programs and inferred cell-cell interactions within the tumor architecture. Finally, given that patients often receive neoadjuvant chemotherapy and/or chemoradiotherapy even in resectable disease, deciphering the gene programs enriched in or induced by cytotoxic therapy will be crucial for developing insights into complementary treatments aimed at eradicating residual cancer cells. Taken together, single-cell and spatial technologies provide an unprecedented opportunity to refine the foundations laid by prior bulk molecular studies and significantly augment precision oncology efforts in pancreatic cancer.

Pan-cancer Transcriptomic Predictors of Perineural Invasion Improve Occult Histopathologic Detection.

PURPOSE: Perineural invasion (PNI) is associated with aggressive tumor behavior, recurrence, and metastasis, and can influence the administration of adjuvant treatment. However, standard histopathologic examination has limited sensitivity in detecting PNI and does not provide insights into its mechanistic underpinnings. EXPERIMENTAL DESIGN: A multivariate Cox regression was performed to validate associations between PNI and survival in 2,029 patients across 12 cancer types. Differential expression and gene set enrichment analysis were used to learn PNI-associated programs. Machine learning models were applied to build a PNI gene expression classifier. A blinded re-review of hematoxylin and eosin (H&E) slides by a board-certified pathologist helped determine whether the classifier could improve occult histopathologic detection of PNI. RESULTS: PNI associated with both poor overall survival [HR, 1.73; 95% confidence interval (CI), 1.27-2.36; P < 0.001] and disease-free survival (HR, 1.79; 95% CI, 1.38-2.32; P < 0.001). Neural-like, prosurvival, and invasive programs were enriched in PNI-positive tumors (P adj < 0.001). Although PNI-associated features likely reflect in part the increased presence of nerves, many differentially expressed genes mapped specifically to malignant cells from single-cell atlases. A PNI gene expression classifier was derived using random forest and evaluated as a tool for occult histopathologic detection. On a blinded H&E re-review of sections initially described as PNI negative, more specimens were reannotated as PNI positive in the high classifier score cohort compared with the low-scoring cohort (P = 0.03, Fisher exact test). CONCLUSIONS: This study provides salient biological insights regarding PNI and demonstrates a role for gene expression classifiers to augment detection of histopathologic features.

Activities and specificities of CRISPR/Cas9 and Cas12a nucleases for targeted mutagenesis in maize.

CRISPR/Cas9 and Cas12a (Cpf1) nucleases are two of the most powerful genome editing tools in plants. In this work, we compared their activities by targeting maize glossy2 gene coding region that has overlapping sequences recognized by both nucleases. We introduced constructs carrying SpCas9-guide RNA (gRNA) and LbCas12a-CRISPR RNA (crRNA) into maize inbred B104 embryos using Agrobacterium-mediated transformation. On-target mutation analysis showed that 90%-100% of the Cas9-edited T0 plants carried indel mutations and 63%-77% of them were homozygous or biallelic mutants. In contrast, 0%-60% of Cas12a-edited T0 plants had on-target mutations. We then conducted CIRCLE-seq analysis to identify genome-wide potential off-target sites for Cas9. A total of 18 and 67 potential off-targets were identified for the two gRNAs, respectively, with an average of five mismatches compared to the target sites. Sequencing analysis of a selected subset of the off-target sites revealed no detectable level of mutations in the T1 plants, which constitutively express Cas9 nuclease and gRNAs. In conclusion, our results suggest that the CRISPR/Cas9 system used in this study is highly efficient and specific for genome editing in maize, while CRISPR/Cas12a needs further optimization for improved editing efficiency.

Defining CRISPR-Cas9 genome-wide nuclease activities with CIRCLE-seq.

Circularization for in vitro reporting of cleavage effects by sequencing (CIRCLE-seq) is a sensitive and unbiased method for defining the genome-wide activity (on-target and off-target) of CRISPR-Cas9 nucleases by selective sequencing of nuclease-cleaved genomic DNA (gDNA). Here, we describe a detailed experimental and analytical protocol for CIRCLE-seq. The principle of our method is to generate a library of circularized gDNA with minimized numbers of free ends. Highly purified gDNA circles are treated with CRISPR-Cas9 ribonucleoprotein complexes, and nuclease-linearized DNA fragments are then ligated to adapters for high-throughput sequencing. The primary advantages of CIRCLE-seq as compared with other in vitro methods for defining genome-wide genome editing activity are (i) high enrichment for sequencing nuclease-cleaved gDNA/low background, enabling sensitive detection with low sequencing depth requirements; and (ii) the fact that paired-end reads can contain complete information on individual nuclease cleavage sites, enabling use of CIRCLE-seq in species without high-quality reference genomes. The entire protocol can be completed in 2 weeks, including time for gRNA cloning, sequence verification, in vitro transcription, library preparation, and sequencing.

In vivo CRISPR editing with no detectable genome-wide off-target mutations.

CRISPR-Cas genome-editing nucleases hold substantial promise for developing human therapeutic applications1-6 but identifying unwanted off-target mutations is important for clinical translation7. A well-validated method that can reliably identify off-targets in vivo has not been described to date, which means it is currently unclear whether and how frequently these mutations occur. Here we describe 'verification of in vivo off-targets' (VIVO), a highly sensitive strategy that can robustly identify the genome-wide off-target effects of CRISPR-Cas nucleases in vivo. We use VIVO and a guide RNA deliberately designed to be promiscuous to show that CRISPR-Cas nucleases can induce substantial off-target mutations in mouse livers in vivo. More importantly, we also use VIVO to show that appropriately designed guide RNAs can direct efficient in vivo editing in mouse livers with no detectable off-target mutations. VIVO provides a general strategy for defining and quantifying the off-target effects of gene-editing nucleases in whole organisms, thereby providing a blueprint to foster the development of therapeutic strategies that use in vivo gene editing.