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Summary: Mapping the spatial interactions of cancer, immune, and stromal cell states presents novel opportunities for patient stratification and for advancing immunotherapy. While single-cell studies revealed significant molecular heterogeneity in prostate cancer cells, the impact of spatial stromal cell heterogeneity remains poorly understood. Here, we used cyclic immunofluorescent imaging on whole-tissue sections to uncover novel spatial associations between cancer and stromal cells in low- and high-grade prostate tumors and tumor-adjacent normal tissues. Our results provide a spatial map of single cells and recurrent cellular neighborhoods in the prostate tumor microenvironment of treatment-naive patients. We report unique populations of mast cells that show distinct spatial associations with M2 macrophages and regulatory T cells. Our results show disease-specific neighborhoods that are primarily driven by androgen receptor-positive (AR+) stromal cells and identify inflammatory gene networks active in AR+ prostate stroma.

Abstract T cell receptor repertoires can be profiled using next generation sequencing (NGS) to measure and monitor adaptive dynamical changes in response to disease and other perturbations. Genomic DNA-based bulk sequencing is cost-effective but necessitates multiplex target amplification using multiple primer pairs with highly variable amplification efficiencies. Here, we utilize an equimolar primer mixture and propose a single statistical normalization step that efficiently corrects for amplification bias post sequencing. Using samples analyzed by both our open protocol and a commercial solution, we show high concordance between bulk clonality metrics. This approach is an inexpensive and open-source alternative to commercial solutions.

Abstract Background: Cancer health research relies on large-scale cohorts to derive generalizable results for different populations. While traditional epidemiological cohorts often use costly random sampling or self-motivated, preselected groups, a shift toward health system-based cohorts has emerged. However, such cohorts depend on participants remaining within a single system. Recent consumer engagement models using smartphone-based communication, driving projects, and social media have begun to upend these paradigms. Methods: We initiated the Healthy Oregon Project (HOP) to support basic and clinical cancer research. HOP study employs a novel, cost-effective remote recruitment approach to effectively establish a large-scale cohort for population-based studies. The recruitment leverages the unique email account, the HOP website, and social media platforms to direct smartphone users to the study app, which facilitates saliva sample collection and survey administration. Monthly newsletters further facilitate engagement and outreach to broader communities. Results: By the end of 2022, the HOP has enrolled approximately 35,000 participants aged 18–100 years (median = 44.2 years), comprising more than 1% of the Oregon adult population. Among those who have app access, ∼87% provided consent to genetic screening. The HOP monthly email newsletters have an average open rate of 38%. Efforts continue to be made to improve survey response rates. Conclusion: This study underscores the efficacy of remote recruitment approaches in establishing large-scale cohorts for population-based cancer studies. The implementation of the study facilitates the collection of extensive survey and biological data into a repository that can be broadly shared and supports collaborative clinical and translational research.

Heskett et al. describe several members of a class of long non-coding RNAs, known as ASARs, which show distinct epigenetic regulation between subclonal lineages and are essential for normal DNA replication timing and stability of human autosomes.

Abstract Cell-free RNA (cfRNA) in plasma reflects phenotypic alterations of both localized sites of cancer and the systemic host response. Here we report that cfRNA sequencing enables the discovery of messenger RNA (mRNA) biomarkers in plasma with the tissue of origin-specific to cancer types and precancerous conditions in both solid and hematologic malignancies. To explore the diagnostic potential of total cfRNA from blood, we sequenced plasma samples of eight hepatocellular carcinoma (HCC) and ten multiple myeloma (MM) patients, 12 patients of their respective precancerous conditions, and 20 non-cancer (NC) donors. We identified distinct gene sets and built classification models using Random Forest and linear discriminant analysis algorithms that could distinguish cancer patients from premalignant conditions and NC individuals with high accuracy. Plasma cfRNA biomarkers of HCC are liver-specific genes and biomarkers of MM are highly expressed in the bone marrow compared to other tissues and are related to cell cycle processes. The cfRNA level of these biomarkers displayed a gradual transition from noncancerous states through precancerous conditions and cancer. Sequencing data were cross-validated by quantitative reverse transcription PCR and cfRNA biomarkers were validated in an independent sample set (20 HCC, 9 MM, and 10 NC) with AUC greater than 0.86. cfRNA results observed in precancerous conditions require further validation. This work demonstrates a proof of principle for using mRNA transcripts in plasma with a small panel of genes to distinguish between cancers, noncancerous states, and precancerous conditions.

High tumour heterogeneity hinders the identification of molecular subtypes in prostate cancer. Here, the authors integrate single-cell chromatin accessibility data with multiplex imaging and reveal distinct chromatin features and transcriptional factor binding signatures in high- and low-grade prostate tumours.

Abstract In a pilot study, we evaluated the feasibility of real-time deep analysis of serial tumor samples from triple negative breast cancer patients to identify mechanisms of resistance and treatment opportunities as they emerge under therapeutic stress engendered by poly-ADP-ribose polymerase (PARP) inhibitors (PARPi). In a BRCA-mutant basal breast cancer exceptional long-term survivor, a striking tumor destruction was accompanied by a marked infiltration of immune cells containing CD8 effector cells, consistent with pre-clinical evidence for association between STING mediated immune activation and benefit from PARPi and immunotherapy. Tumor cells in the exceptional responder underwent extensive protein network rewiring in response to PARP inhibition. In contrast, there were minimal changes in the ecosystem of a luminal androgen receptor rapid progressor, likely due to indifference to the effects of PARP inhibition. Together, identification of PARPi-induced emergent changes could be used to select patient specific combination therapies, based on tumor and immune state changes.

Collection and analysis of circulating tumor DNA (ctDNA) is one of the few methods of liquid biopsy that measures generalizable and tumor specific molecules, and is one of the most promising approaches in assessing the effectiveness of cancer care. Clinical assays that utilize ctDNA are commercially available for the identification of actionable mutations prior to treatment and to assess minimal residual disease after treatment. There is currently no clinical ctDNA assay specifically intended to monitor disease response during treatment, partially due to the complex challenge of understanding the biological sources of ctDNA and the underlying principles that govern its release. Although studies have shown pre- and post-treatment ctDNA levels can be prognostic, there is evidence that early, on-treatment changes in ctDNA levels are more accurate in predicting response. Yet, these results also vary widely among cohorts, cancer type, and treatment, likely due to the driving biology of tumor cell proliferation, cell death, and ctDNA clearance kinetics. To realize the full potential of ctDNA monitoring in cancer care, we may need to reorient our thinking toward the fundamental biological underpinnings of ctDNA release and dissemination from merely seeking convenient clinical correlates.

Abstract Many emerging technologies are reliant on circulating cell-free DNA (cfDNA) and cell-free RNA (cfRNA) applications in the clinic. However, the impact of diurnal cycles or daily meals on circulating analytes are poorly understood and may be confounding factors when developing diagnostic platforms. To begin addressing this knowledge gap, we obtained plasma from four healthy donors serially sampled five times during 12 h in a single day. For all samples, we measured concentrations of cfDNA and cfRNA using both bulk measurements and gene-specific digital droplet PCR. We found no significant variation attributed to blood draw number for the cfDNA or cfRNA. This indicated that natural diurnal cycles and meal consumption do not appear to significantly affect abundance of total cfDNA, total cfRNA, or our two selected cfRNA transcripts. Conversely, we observed significant variation between individual donors for cfDNA and one of the cfRNA transcripts. The results of this work suggest that it will be important to consider patient-specific baselines when designing reliable circulating cfDNA or cfRNA clinical assays.

Despite the availability of multiple human epidermal growth factor receptor 2–targeted (HER2-targeted) treatments, therapeutic resistance in HER2+ breast cancer remains a clinical challenge. Intratumor heterogeneity for HER2 and resistance-conferring mutations in the PIK3CA gene (encoding PI3K catalytic subunit α) have been investigated in response and resistance to HER2-targeting agents, while the role of divergent cellular phenotypes and tumor epithelial-stromal cell interactions is less well understood. Here, we assessed the effect of intratumor cellular genetic heterogeneity for ERBB2 (encoding HER2) copy number and PIK3CA mutation on different types of neoadjuvant HER2-targeting therapies and clinical outcome in HER2+ breast cancer. We found that the frequency of cells lacking HER2 was a better predictor of response to HER2-targeted treatment than intratumor heterogeneity. We also compared the efficacy of different therapies in the same tumor using patient-derived xenograft models of heterogeneous HER2+ breast cancer and single-cell approaches. Stromal determinants were better predictors of response than tumor epithelial cells, and we identified alveolar epithelial and fibroblastic reticular cells as well as lymphatic vessel endothelial hyaluronan receptor 1–positive (Lyve1+) macrophages as putative drivers of therapeutic resistance. Our results demonstrate that both preexisting and acquired resistance to HER2-targeting agents involve multiple mechanisms including the tumor microenvironment. Furthermore, our data suggest that intratumor heterogeneity for HER2 should be incorporated into treatment design.

Summary: We present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's traditional approach of explaining changes in data using prior knowledge but does this at the scale of hundreds of thousands of reactions. This is a specific example of how to automate scientific reasoning processes and illustrates the power of mapping from experimental data to prior knowledge via logic programming. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at http://causalpath.org. The bigger picture: Molecular profiling of biological organisms provides us with a great amount of information on cellular differences, but converting it to mechanistic insights is still a very challenging task. A prominent approach is to integrate new measurements with the mechanistic knowledge described in the scientific literature and build a model that is supported by both. Although this can be done in many ways, an adept approach will use the literature knowledge in detail and follow high standards of logical reasoning while integrating the known and the new. This article describes an approach that utilizes the details in human biological pathways to identify pairs of changes with a likely cause-effect relation within. The approach automatically converts comparative proteomic and other molecular profiles into hypotheses of differentially active mechanistic relations that explain how the profiles came to be.

Resistance to therapy can be driven by intratumoral heterogeneity. Here, the authors show that drug tolerant persistent cell populations emerge during treatment, and these emergent populations arise through epigenetically mediated cell state transitions rather than sub population selection.

Long non-coding RNAs (lncRNA) comprise a diverse group of non-protein-coding RNAs >200 bp in length that are involved in various normal cellular processes and disease states, and can affect coding gene expression through mechanisms in cis or in trans. Since the discovery of the first functional lncRNAs transcribed by RNA Polymerase II, H19 and Xist, many others have been identified and noted for their unusual transcriptional pattern, whereby expression from one chromosome homolog is strongly favored over the other, also known as mono-allelic or differential allelic expression. lncRNAs with differential allelic expression have been observed to play critical roles in developmental gene regulation, chromosome structure, and disease. Here, we will focus on known examples of differential allelic expression of lncRNAs and highlight recent research describing functional lncRNAs expressed from both imprinted and random mono-allelic expression domains.

As non-operative management (NOM) of esophageal and rectal cancer is becoming more prevalent, blood-biomarkers such as circulating tumor DNA (ctDNA) may provide clinical information in addition to endoscopy and imaging to aid in treatment decisions following chemotherapy and radiation therapy. In this feasibility study, we prospectively collected plasma samples from locally advanced esophageal (n = 3) and rectal cancer (n = 2) patients undergoing multimodal neoadjuvant therapy to assess the feasibility of serial ctDNA monitoring throughout neoadjuvant therapy. Using the Dual-Index Degenerate Adaptor-Sequencing (DIDA-Seq) error-correction method, we serially interrogated plasma cell-free DNA at 28–41 tumor-specific genomic loci throughout therapy and in surveillance with an average limit of detection of 0.016% mutant allele frequency. In both rectal cancer patients, ctDNA levels were persistently elevated following total neoadjuvant therapy with eventual detection of clinical recurrence prior to salvage surgery. Among the esophageal cancer patients, ctDNA levels closely correlated with tumor burden throughout and following neoadjuvant therapy, which was associated with a pathologic complete response in one patient. In this feasibility study, patient- and tumor-specific ctDNA levels correlated with clinical outcomes throughout multi-modality therapy suggesting that serial monitoring of patient ctDNA has the potential to serve as a highly sensitive and specific biomarker to risk-stratify esophageal and rectal cancer patients eligible for NOM. Further prospective investigation is warranted.

Analysing multiple tumours from the same patient permits the study of the germline contribution to cancer. Here, the authors sequence multiple renal tumours from VHL patients and find that intra-patient tumours are clonally distinct but share some genetic features, suggesting that patient-specific factors influence tumour formation.

Summary: Renal cell carcinoma (RCC) is not a single disease, but several histologically defined cancers with different genetic drivers, clinical courses, and therapeutic responses. The current study evaluated 843 RCC from the three major histologic subtypes, including 488 clear cell RCC, 274 papillary RCC, and 81 chromophobe RCC. Comprehensive genomic and phenotypic analysis of the RCC subtypes reveals distinctive features of each subtype that provide the foundation for the development of subtype-specific therapeutic and management strategies for patients affected with these cancers. Somatic alteration of BAP1, PBRM1, and PTEN and altered metabolic pathways correlated with subtype-specific decreased survival, while CDKN2A alteration, increased DNA hypermethylation, and increases in the immune-related Th2 gene expression signature correlated with decreased survival within all major histologic subtypes. CIMP-RCC demonstrated an increased immune signature, and a uniform and distinct metabolic expression pattern identified a subset of metabolically divergent (MD) ChRCC that associated with extremely poor survival. : Ricketts et al. find distinctive features of each RCC subtype, providing the foundation for development of subtype-specific therapeutic and management strategies. Somatic alteration of BAP1, PBRM1, and metabolic pathways correlates with subtype-specific decreased survival, while CDKN2A alteration, DNA hypermethylation, and Th2 immune signature correlate with decreased survival within all subtypes. Keywords: clear cell renal cell carcinoma, papillary renal cell carcinoma, chromophobe renal cell carcinoma, CDKN2A, DNA hypermethylation, immune signature, chromatin remodeling, TCGA, PanCanAtlas

Somatic mutations in cancer are more frequent in heterochromatic and late-replicating regions of the genome. We report that regional disparities in mutation density are virtually abolished within transcriptionally silent genomic regions of cutaneous squamous cell carcinomas (cSCCs) arising in an XPC−/− background. XPC−/− cells lack global genome nucleotide excision repair (GG-NER), thus establishing differential access of DNA repair machinery within chromatin-rich regions of the genome as the primary cause for the regional disparity. Strikingly, we find that increasing levels of transcription reduce mutation prevalence on both strands of gene bodies embedded within H3K9me3-dense regions, and only to those levels observed in H3K9me3-sparse regions, also in an XPC-dependent manner. Therefore, transcription appears to reduce mutation prevalence specifically by relieving the constraints imposed by chromatin structure on DNA repair. We model this relationship among transcription, chromatin state, and DNA repair, revealing a new, personalized determinant of cancer risk.