Identification of Spatial Proteomic Signatures of Colon Tumor Metastasis: A Digital Spatial Profiling Approach

Artificial Intelligence and Machine Learning Innovations to Improve Design and Representativeness in Oncology Clinical Trials

The integration of artificial intelligence (AI) and machine learning (ML) in oncology clinical trials is rapidly evolving alongside the broader field. For example, AI-driven adaptive trial designs may allow for real-time modifications based on emerging safety and efficacy signals, enabling more responsive and efficient trials. AI-powered diagnostic tools, including radiomics, computational pathology, and spatial omics, can improve trial patient selection and response assessments. ML-based patient outcome simulations can similarly enhance patient stratification strategies and statistical power. Application of AI can also improve the accessibility of real-world data, including opportunities to enhance data extraction, standardization, and harmonization of data from routine clinical practice. Data generated from digital health technologies (eg, wearable devices, electronic sensors, computing platforms, software applications) may enable a more comprehensive understanding of patient populations to support clinical trials from enrollment to assessment. Automation of trial operations and data management can also improve data fidelity and decrease investigator burden, which has the potential to streamline trial execution and increase potential use of decentralization. There are ongoing efforts to enhance regulatory clarity, mitigate bias, and uphold ethical use of these novel technologies. In this article, we review use cases of AI and ML in oncology clinical trials, including their role in patient recruitment, trial design and operations, data management, and diagnostics. Although these technologies can have applications across all phases of drug development including early discovery, we focus on phase II and III trials, where AI and ML may have a pronounced ability to enhance trial efficiency, patient stratification, and regulatory decision making. By integrating AI and ML, clinical trials can become more adaptive, data-driven, and inclusive in the pursuit of improving patient outcomes.

Spatially Resolved, Multiregion Proteomics for Prediction of Immunotherapy Outcome in Deficient Mismatch Repair Metastatic Colorectal Cancer

PURPOSE

Digital proteomic profiling was performed to identify spatial context in relationship to patient response and survival after anti-PD-1 therapy in metastatic colorectal cancer.

EXPERIMENTAL DESIGN

Primary colorectal cancers with deficient mismatch repair from patients treated with anti-PD-1 antibodies were analyzed (N = 30) using digital spatial profiling (GeoMx nCounter). At the invasive margin, 71 proteins were profiled in 10 regions of interest/slide that were segmented into 3 compartments labeled with pan-cytokeratin (epithelia), CD45 (stromal cells), and SYTO13 (nuclei). In an independent cohort (n = 13), digital spatial profiling data and single-cell transcriptomic data were analyzed. Differential protein abundance, after Benjamini-Hochberg correction, was examined by clinical response and progression-free survival (PFS) using multivariable Cox regression.

RESULTS

Protein abundance varied significantly between epithelial and stromal compartments. Nonresponders to anti-PD-1 showed higher fibronectin and smooth muscle actin abundance in the epithelial compartment that was associated with significantly shorter PFS (adjusted HR: 6.49 and 4.52, respectively; P < 0.05). In CD45+ stroma, increased expression of proteins related to T cells (CD3 and CD4), NK cells (CD56), antigen presentation (CD40), immune activation (CD27, ICOS), and apoptosis (GZMA) were found in responders (vs nonresponders) to anti-PD-1; each marker was significantly associated with longer patient PFS (0.02 < adjusted HR < 0.17; P < 0.05). In a separate cohort, consistent results by compartment were found for fibronectin and CD56. Gene expression data revealed that fibronectin and smooth muscle actin were primarily derived from cancer-associated fibroblasts.

CONCLUSIONS

Spatially resolved protein profiles within microenvironments of deficient mismatch repair colorectal cancers can influence patient response and survival after anti-PD-1, highlighting their potential clinical significance.

Histology-Based Virtual RNA Inference Identifies Pathways Associated with Metastasis Risk in Colorectal Cancer

Colorectal cancer (CRC) remains a major health concern, with over 150,000 new diagnoses and more than 50,000 deaths annually in the United States, underscoring an urgent need for improved screening, prognostication, disease management, and therapeutic approaches. The tumor microenvironment (TME)-comprising cancerous and immune cells interacting within the tumor's spatial architecture-plays a critical role in disease progression and treatment outcomes, reinforcing its importance as a prognostic marker for metastasis and recurrence risk. However, traditional methods for TME characterization, such as bulk transcriptomics and multiplex protein assays, lack sufficient spatial resolution. Although spatial transcriptomics (ST) allows for the high-resolution mapping of whole transcriptomes at near-cellular resolution, current ST technologies (e.g., Visium, Xenium) are limited by high costs, low throughput, and issues with reproducibility, preventing their widespread application in large-scale molecular epidemiology studies. In this study, we refined and implemented Virtual RNA Inference (VRI) to derive ST-level molecular information directly from hematoxylin and eosin (H&E)-stained tissue images. Our VRI models were trained on the largest matched CRC ST dataset to date, comprising 45 patients and more than 300,000 Visium spots from primary tumors. Using state-of-the-art architectures (UNI, ResNet-50, ViT, and VMamba), we achieved a median Spearman's correlation coefficient of 0.546 between predicted and measured spot-level expression. As validation, VRI-derived gene signatures linked to specific tissue regions (tumor, interface, submucosa, stroma, serosa, muscularis, inflammation) showed strong concordance with signatures generated via direct ST, and VRI performed accurately in estimating cell-type proportions spatially from H&E slides. In an expanded CRC cohort controlling for tumor invasiveness and clinical factors, we further identified VRI-derived gene signatures significantly associated with key prognostic outcomes, including metastasis status. Although certain tumor-related pathways are not fully captured by histology alone, our findings highlight the ability of VRI to infer a wide range of "histology-associated" biological pathways at near-cellular resolution without requiring ST profiling. Future efforts will extend this framework to expand TME phenotyping from standard H&E tissue images, with the potential to accelerate translational CRC research at scale.

Association of pre-diagnosis plasma proteomic contexture with overall survival of early- and late-stage colon cancer patients

BACKGROUND

The pre-diagnosis plasma proteomic contexture of colon cancer patients may reflect host immune and biological conditions and potentially associate with survival outcomes. We aimed to characterize pre-diagnosis proteomic contextures in colon cancer patients and determine potential association with overall survival of the patients.

METHODS

Baseline plasma samples collected at an average of 7.90 years before diagnosis from colon cancer patients in the UK Biobank cohort were analyzed using Olink proteomics technology. Cox-regression analysis was applied to identify distinct pre-diagnosis proteomic contextures and determine their association with survival outcomes.

RESULTS

In early-stage colon cancer, a 10-protein pre-diagnosis profile was identified, involving biological processes of extracellular matrix remodeling and immune evasion through deregulation of innate immune activation. Increased activity in these pathways before diagnosis was associated with poor survival outcomes. In late-stage cases, an 8-protein pre-diagnosis profile was linked to pathways involving in cell adhesion, angiogenesis, and pro-inflammatory response. Similarly, heightened activity in these pathways prior to diagnosis correlated with worse survival. When combined with two demographic factors age and sex, these proteomic profiles demonstrated strong predictive associations with survival outcomes at multiple time points post-diagnosis. The area under the receiver operating characteristic curve values were 0.85, 0.82, and 0.89 for early-stage cancer at 1, 5, and 10 years, respectively, and 0.71, 0.72, and 0.79 for late-stage cancer over the same periods.

CONCLUSIONS

Biological processes like extracellular matrix remodeling and pro-inflammatory response are active well before diagnosis and may play a critical role in shaping colon cancer progression.

Tumor infiltrating lymphocytes (TILs) - Pathologia, quo vadis? - A global survey

Tumor-infiltrating lymphocytes (TILs) and the tumor microenvironment have become increasingly important in cancer research, and immunotherapy has achieved major breakthroughs in improving patient outcomes. Despite the significant role of the pathologist in identifying, subtyping and reporting TILs, the implementation and assessment of TILs in pathology routine remains vague. To assess the actual use of TILs in routine clinical practice, a formal standardized questionnaire was disseminated on two social media platforms ("X" and LinkedIn) and by email in June 2024. Based on the results, we conducted a literature review on TILs via Medline/Pubmed in the two most scored and reported entities, namely malignant melanoma and colorectal cancer (CRC). 77 participants from 24 different countries around the world, mostly pathologists (n = 63, 82.0 %), completed the survey. More than half of the participants do not assess or report TILs in their daily (clinical) practice, a trend consistent across the countries included in the study. A variety of methods are used to report TILs, ranging from Artificial Intelligence (AI)-based scoring algorithms to quantification by eyeballing. Despite recognizing the importance of TIL assessment in clinical routine, many participants find it time-consuming and express a strong preference for AI-based quantification. Our survey reflects the perspective of mostly early career pathologists who recognize the importance of TILs in cancer but face challenges in implementation. The development of AI tools and consensus guidelines could alleviate these barriers. In addition, increasing the visibility and understanding of the role of pathologists within the medical community remains critical.

Signaling pathways involved in colorectal cancer: pathogenesis and targeted therapy

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Its complexity is influenced by various signal transduction networks that govern cellular proliferation, survival, differentiation, and apoptosis. The pathogenesis of CRC is a testament to the dysregulation of these signaling cascades, which culminates in the malignant transformation of colonic epithelium. This review aims to dissect the foundational signaling mechanisms implicated in CRC, to elucidate the generalized principles underpinning neoplastic evolution and progression. We discuss the molecular hallmarks of CRC, including the genomic, epigenomic and microbial features of CRC to highlight the role of signal transduction in the orchestration of the tumorigenic process. Concurrently, we review the advent of targeted and immune therapies in CRC, assessing their impact on the current clinical landscape. The development of these therapies has been informed by a deepening understanding of oncogenic signaling, leading to the identification of key nodes within these networks that can be exploited pharmacologically. Furthermore, we explore the potential of integrating AI to enhance the precision of therapeutic targeting and patient stratification, emphasizing their role in personalized medicine. In summary, our review captures the dynamic interplay between aberrant signaling in CRC pathogenesis and the concerted efforts to counteract these changes through targeted therapeutic strategies, ultimately aiming to pave the way for improved prognosis and personalized treatment modalities in colorectal cancer.

The burgeoning spatial multi-omics in human gastrointestinal cancers

The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand the molecular mechanisms governing individual development and disease progression, precise acquisition of biological data, including genome, transcriptome, proteome, metabolome, and epigenome, with single-cell resolution and spatial information within the body's three-dimensional context, is essential. This foundational information serves as the basis for deciphering cellular and molecular mechanisms. Although single-cell multi-omics technology can provide biological information such as genome, transcriptome, proteome, metabolome, and epigenome with single-cell resolution, the sample preparation process leads to the loss of spatial information. Spatial multi-omics technology, however, facilitates the characterization of biological data, such as genome, transcriptome, proteome, metabolome, and epigenome in tissue samples, while retaining their spatial context. Consequently, these techniques significantly enhance our understanding of individual development and disease pathology. Currently, spatial multi-omics technology has played a vital role in elucidating various processes in tumor biology, including tumor occurrence, development, and metastasis, particularly in the realms of tumor immunity and the heterogeneity of the tumor microenvironment. Therefore, this article provides a comprehensive overview of spatial transcriptomics, spatial proteomics, and spatial metabolomics-related technologies and their application in research concerning esophageal cancer, gastric cancer, and colorectal cancer. The objective is to foster the research and implementation of spatial multi-omics technology in digestive tumor diseases. This review will provide new technical insights for molecular biology researchers.

Digital spatial profiling to predict recurrence in grade 3 stage I lung adenocarcinoma

OBJECTIVE

Early-stage lung adenocarcinoma is treated with local therapy alone, although patients with grade 3 stage I lung adenocarcinoma have a 50% 5-year recurrence rate. Our objective is to determine if analysis of the tumor microenvironment can create a predictive model for recurrence.

METHODS

Thirty-four patients with grade 3 stage I lung adenocarcinoma underwent surgical resection. Digital spatial profiling was used to perform genomic (n = 31) and proteomic (n = 34) analyses of pancytokeratin positive and negative tumor cells. K-means clustering was performed on the top 50 differential genes and top 20 differential proteins, with Kaplan-Meier recurrence curves based on patient clustering. External validation of high-expression genes was performed with Kaplan-Meier plotter.

RESULTS

There were no significant clinicopathologic differences between patients who did (n = 14) and did not (n = 20) have recurrence. Median time to recurrence was 806 days; median follow-up with no recurrence was 2897 days. K-means clustering of pancytokeratin positive genes resulted in a model with a Kaplan-Meier curve with concordance index of 0.75. K-means clustering for pancytokeratin negative genes was less successful at differentiating recurrence (concordance index 0.6). Genes upregulated or downregulated for recurrence were externally validated using available public databases. Proteomic data did not reach statistical significance but did internally validate the genomic data described.

CONCLUSIONS

Genomic difference in lung adenocarcinoma may be able to predict risk of recurrence. After further validation, stratifying patients by this risk may help guide who will benefit from adjuvant therapy.

Precision medicine in colorectal cancer: Leveraging multi-omics, spatial omics, and artificial intelligence

Colorectal cancer (CRC) is a leading cause of cancer-related deaths. Recent advancements in genomic technologies and analytical approaches have revolutionized CRC research, enabling precision medicine. This review highlights the integration of multi-omics, spatial omics, and artificial intelligence (AI) in advancing precision medicine for CRC. Multi-omics approaches have uncovered molecular mechanisms driving CRC progression, while spatial omics have provided insights into the spatial heterogeneity of gene expression in CRC tissues. AI techniques have been utilized to analyze complex datasets, identify new treatment targets, and enhance diagnosis and prognosis. Despite the tumor's heterogeneity and genetic and epigenetic complexity, the fusion of multi-omics, spatial omics, and AI shows the potential to overcome these challenges and advance precision medicine in CRC. The future lies in integrating these technologies to provide deeper insights and enable personalized therapies for CRC patients.

Upregulation of EMR1 (ADGRE1) by Tumor-Associated Macrophages Promotes Colon Cancer Progression by Activating the JAK2/STAT1,3 Signaling Pathway in Tumor Cells

Previously, we reported that epidermal growth factor-like module-containing mucin-like hormone receptor-like 1 (EMR1/ADGRE1) is abnormally expressed in colon cancer (CC) and is a risk factor for lymph node metastasis (LNM) and poor recurrence-free survival in patients with abundant tumor-associated macrophages (TAMs). However, the signaling pathways associated with EMR1 expression in CC progression remain unclear. In this study, we aimed to explore the role of EMR1 and its signaling interactions with macrophages in CC progression. Spatial transcriptomics of pT3 microsatellite unstable CC tissues revealed heightened Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling in EMR1-HL CC with LNM compared to EMR1-N CC without LNM. Through in vitro coculture of CC cells with macrophages, EMR1 expression by CC cells was found to be induced by TAMs, ultimately interacting with upregulated JAK/STAT signaling, increasing cell proliferation, migration, and motility, and reducing apoptosis. JAK2/STAT3 inhibition decreased the levels of EMR1, JAK2, STAT1, and STAT3, significantly impeded the proliferation, migration, and mobility of cells, and increased the apoptosis of EMR1+ CC cells compared to their EMR1KO counterparts. Overall, TAMs-induced EMR1 upregulation in CC cells may promote LNM and CC progression via JAK2/STAT1,3 signaling upregulation. This study provides further insights into the molecular mechanisms involving macrophages and intracellular EMR1 expression in CC progression, suggesting its clinical significance and offering potential interventions to enhance patient outcomes.

Identification of a Twelve-microRNA Signature with Prognostic Value in Stage II Microsatellite Stable Colon Cancer

We aimed to identify and validate a set of miRNAs that could serve as a prognostic signature useful to determine the recurrence risk for patients with COAD. Small RNAs from tumors of 100 stage II, untreated, MSS colon cancer patients were sequenced for the discovery step. For this purpose, we built an miRNA score using an elastic net Cox regression model based on the disease-free survival status. Patients were grouped into high or low recurrence risk categories based on the median value of the score. We then validated these results in an independent sample of stage II microsatellite stable tumor tissues, with a hazard ratio of 3.24, (CI_95% = 1.05-10.0) and a 10-year area under the receiver operating characteristic curve of 0.67. Functional analysis of the miRNAs present in the signature identified key pathways in cancer progression. In conclusion, the proposed signature of 12 miRNAs can contribute to improving the prediction of disease relapse in patients with stage II MSS colorectal cancer, and might be useful in deciding which patients may benefit from adjuvant chemotherapy.