Abstract Replication timing is a costly but powerful tool for characterizing cellular mechanisms that underlie chromatin organization, cancer epigenetics, and genomic instability. Genome-wide replication timing profiles reflect the temporal order of DNA synthesis during S phase and are closely linked to chromatin accessibility, transcriptional activity, and proliferative state. Prior work has demonstrated a robust inverse relationship between replication timing and DNA methylation at the domain scale, enabling methylation-based proxies to approximate replication timing in large cohorts where direct experimental measurement is impractical. Given the tight coupling between replication timing, chromatin structure, and cellular phenotype, we hypothesized that histologic morphology encodes information consistent with replication timing states. To test this hypothesis, we implemented Vision Transformer (ViT) architectures to predict replication timing proxies from whole-slide histopathology images. Patch-level embeddings were extracted using a pretrained ViT and aggregated through attention-based multiple instance learning to predict sample-level replication timing. In parallel, CellViT models were employed to perform cell-level prediction, enabling direct comparison between patch-based and cellular representations. Across both modeling strategies, statistically significant correlations were observed between image-derived features and methylation-based replication timing proxies. Patch-level models achieved correlations of approximately 46%, while cell-level models consistently reached correlations exceeding 50% on the validation cohort. Prediction error remained stable across folds, with mean absolute error and mean squared error values ranging between 0.4 and 0.6. These results demonstrate that replication timing–associated epigenomic states are reflected in tissue morphology and can be inferred using deep learning models applied to routine histopathology. This work establishes a feasible, noninvasive framework for replicosomic inference from whole-slide images and supports future efforts toward spatially resolved replication timing analysis and integrative modeling of replicative stress in cancer.
79Eliminating tissue-fold artifacts in histopathological whole-slide images for improved image-based prediction of cancer grade
Eliminating tissue-fold artifacts in histopathological whole-slide images for improved image-based prediction of cancer grade
Read more6Integration of Deep Learning and Graph Theory for Analyzing Histopathology Whole-slide Images
Characterization of collagen deposition in immunostained images is relevant to various pathological conditions, particularly in human immunodeficiency virus (HIV) infection. Accurate segmentation of these collagens and extracting representative features of underlying diseases are important steps to achieve quantitative diagnosis. While a first order statistic derived from the segmented collagens can be useful in representing pathological evolutions at different timepoints, it fails to capture morphological changes and spatial arrangements. In this work, we demonstrate a complete pipeline for extracting key histopathology features representing underlying disease progression from histopathology whole-slide images (WSIs) via integration of deep learning and graph theory. A convolutional neural network is trained and utilized for histopathological WSI segmentation. Parallel processing is applied to convert 100K ~ 150K segmented collagen fibrils into a single collective attributed relational graph, and graph theory is applied to extract topological and relational information from the collagenous framework. Results are in good agreement with the expected pathogenicity induced by collagen deposition, highlighting potentials in clinical applications for analyzing various meshwork-structures in whole-slide histology images.
Read more
32Deep Learning-Based Classification of Uterine Cervical and Endometrial Cancer Subtypes from Whole-Slide Histopathology Images
Uterine cervical and endometrial cancers have different subtypes with different clinical outcomes. Therefore, cancer subtyping is essential for proper treatment decisions. Furthermore, an endometrial and endocervical origin for an adenocarcinoma should also be distinguished. Although the discrimination can be helped with various immunohistochemical markers, there is no definitive marker. Therefore, we tested the feasibility of deep learning (DL)-based classification for the subtypes of cervical and endometrial cancers and the site of origin of adenocarcinomas from whole slide images (WSIs) of tissue slides. WSIs were split into 360 × 360-pixel image patches at 20× magnification for classification. Then, the average of patch classification results was used for the final classification. The area under the receiver operating characteristic curves (AUROCs) for the cervical and endometrial cancer classifiers were 0.977 and 0.944, respectively. The classifier for the origin of an adenocarcinoma yielded an AUROC of 0.939. These results clearly demonstrated the feasibility of DL-based classifiers for the discrimination of cancers from the cervix and uterus. We expect that the performance of the classifiers will be much enhanced with an accumulation of WSI data. Then, the information from the classifiers can be integrated with other data for more precise discrimination of cervical and endometrial cancers.
Read more59Whole-Slide Image Analysis Reveals Quantitative Landscape of Tumor-Immune Microenvironment in Colorectal Cancers.
Despite the well-known prognostic value of the tumor-immune microenvironment (TIME) in colorectal cancers, objective and readily applicable methods for quantifying tumor-infiltrating lymphocytes (TIL) and the tumor-stroma ratio (TSR) are not yet available. We established an open-source software-based analytic pipeline for quantifying TILs and the TSR from whole-slide images obtained after CD3 and CD8 IHC staining. Using a random forest classifier, the method separately quantified intraepithelial TILs (iTIL) and stromal TILs (sTIL). We applied this method to discovery and validation cohorts of 578 and 283 stage III or high-risk stage II colorectal cancers patients, respectively, who were subjected to curative surgical resection and oxlaliplatin-based adjuvant chemotherapy. Automatic quantification of iTILs and sTILs showed a moderate concordance with that obtained after visual inspection by a pathologist. The K-means-based consensus clustering of 197 TIME parameters that showed robustness against interobserver variations caused colorectal cancers to be grouped into five distinctive subgroups, reminiscent of those for consensus molecular subtypes (CMS1-4 and mixed/intermediate group). In accordance with the original CMS report, the CMS4-like subgroup (cluster 4) was significantly associated with a worse 5-year relapse-free survival and proved to be an independent prognostic factor. The clinicopathologic and prognostic features of the TIME subgroups have been validated in an independent validation cohort. Machine-learning-based image analysis can be useful for extracting quantitative information about the TIME, using whole-slide histopathologic images. This information can classify colorectal cancers into clinicopathologically relevant subgroups without performing a molecular analysis of the tumors.
Read more1Abstract 4938: Prediction of gene mutation from colorectal adenocarcinoma whole slide images via integrated deep learning pipeline
Colorectal cancer (CRC) stands as one of the deadliest cancer types worldwide. Identifying specific gene mutations crucial for targeted therapy is pivotal for CRC treatment. However, the current genetic testing method involves liquid biopsy, which is often cumbersome and cost-intensive. Histopathological whole-slide images (WSIs) are routinely generated for diagnostic purposes and can offer valuable insights into cellular heterogeneity. In this study, we aim to develop an integrated deep learning pipeline for predicting TP53 mutations in CRC, one of the most frequently mutated genes in CRC. We commenced by collecting 424 diagnostic WSIs from the TCGA-COAD cohorts. Each WSI was split as small patches at a resolution of 0.5 micrometers per pixel for following analyses. Quality control was implemented using Otsu thresholding and Gaussian blur filtering, both at the slide-level masking and patch-level filtering stages. Additionally, we applied the Macenko algorithm for color normalization to eliminate systematic errors. Following image preprocessing, we constructed a tissue-type classification model based on VGG19, which was trained and tested using the NCT-CRC-HE-100K and CRC-VAL-HE-7K datasets, both containing pixels with tissue types labeled by human experts. The trained classifier was then used to identify adenocarcinoma epithelium patches from TCGA-COAD. Subsequently, we applied multiple pre-trained Convolutional Neural Network (CNN) models, including ResNet-101, DenseNet-201, and Inception-ResNet-v2, to predict TP53 mutations for the identified adenocarcinoma epithelium patches from TCGA-COAD. A novel integration approach was employed to aggregate patch-level predictions into slide-level outcomes. We evaluated the trained models using various metrics, including accuracy, precision, recall, F1 score, AUC (Area Under the Curve), and Cohen's Kappa coefficient. The tissue-type classification model exhibited robust performance, achieving an overall accuracy of 94.7%, a Cohen's Kappa coefficient of 94%, a precision of 95.3%, a recall of 99.76%, and an F1 score of 97.48% for identifying adenocarcinoma epithelium patches. Regarding the TP53 mutation prediction models, ResNet-101, DenseNet-201, and Inception-ResNet-v2 exhibit the predictive ability to both patch level (AUC=65%) and slide level (AUC=70%). In summary, the proposed deep learning pipeline offers an efficient way to acquire gene mutation information from WSIs. Moreover, it demonstrates a capability to discover potential molecular aberration from the clinical images. Citation Format: Geng-Yun Tien, Liang-Chuan Lai, Tzu-Pin Lu, Mong-Hsun Tsai, Hsiang-Han Chen, Eric Y. Chuang. Prediction of gene mutation from colorectal adenocarcinoma whole slide images via integrated deep learning pipeline [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4938.
Read more3A correlation graph attention network for classifying chromosomal instabilities from histopathology whole-slide images
A correlation graph attention network for classifying chromosomal instabilities from histopathology whole-slide images
297Stain Specific Standardization of Whole-Slide Histopathological Images.
Variations in the color and intensity of hematoxylin and eosin (H&E) stained histological slides can potentially hamper the effectiveness of quantitative image analysis. This paper presents a fully automated algorithm for standardization of whole-slide histopathological images to reduce the effect of these variations. The proposed algorithm, called whole-slide image color standardizer (WSICS), utilizes color and spatial information to classify the image pixels into different stain components. The chromatic and density distributions for each of the stain components in the hue-saturation-density color model are aligned to match the corresponding distributions from a template whole-slide image (WSI). The performance of the WSICS algorithm was evaluated on two datasets. The first originated from 125 H&E stained WSIs of lymph nodes, sampled from 3 patients, and stained in 5 different laboratories on different days of the week. The second comprised 30 H&E stained WSIs of rat liver sections. The result of qualitative and quantitative evaluations using the first dataset demonstrate that the WSICS algorithm outperforms competing methods in terms of achieving color constancy. The WSICS algorithm consistently yields the smallest standard deviation and coefficient of variation of the normalized median intensity measure. Using the second dataset, we evaluated the impact of our algorithm on the performance of an already published necrosis quantification system. The performance of this system was significantly improved by utilizing the WSICS algorithm. The results of the empirical evaluations collectively demonstrate the potential contribution of the proposed standardization algorithm to improved diagnostic accuracy and consistency in computer-aided diagnosis for histopathology data.
Read moreData from Development of a Histopathology Informatics Pipeline for Classification and Prediction of Clinical Outcomes in Subtypes of Renal Cell Carcinoma
<div>AbstractPurpose:<p>Histopathology evaluation is the gold standard for diagnosing clear cell (ccRCC), papillary, and chromophobe renal cell carcinoma (RCC). However, interrater variability has been reported, and the whole-slide histopathology images likely contain underutilized biological signals predictive of genomic profiles.</p>Experimental Design:<p>To address this knowledge gap, we obtained whole-slide histopathology images and demographic, genomic, and clinical data from The Cancer Genome Atlas, the Clinical Proteomic Tumor Analysis Consortium, and Brigham and Women's Hospital (Boston, MA) to develop computational methods for integrating data analyses. Leveraging these large and diverse datasets, we developed fully automated convolutional neural networks to diagnose renal cancers and connect quantitative pathology patterns with patients' genomic profiles and prognoses.</p>Results:<p>Our deep convolutional neural networks successfully detected malignancy (AUC in the independent validation cohort: 0.964–0.985), diagnosed RCC histologic subtypes (independent validation AUCs of the best models: 0.953–0.993), and predicted stage I ccRCC patients' survival outcomes (log-rank test <i>P</i> = 0.02). Our machine learning approaches further identified histopathology image features indicative of copy-number alterations (AUC > 0.7 in multiple genes in patients with ccRCC) and tumor mutation burden.</p>Conclusions:<p>Our results suggest that convolutional neural networks can extract histologic signals predictive of patients' diagnoses, prognoses, and genomic variations of clinical importance. Our approaches can systematically identify previously unknown relations among diverse data modalities.</p></div>
Read moreDNA methylation and lncRNA control asynchronous DNA replication at specific imprinted gene domains.
Besides genome-wide patterns of replication timing (RT), some genes display allelic replication asynchrony in stem cells, brought about by stochastic events and genetic polymorphisms. Whether epigenetic modifications control asynchronous replication remains unclear. Here, we explore domains controlled by genomic imprinting, where parental DNA methylation imprints mediate allele-specific gene expression. Our genome-wide and locus-specific assays in mono-parental and hybrid mouse ESCs reveal pronounced RT asynchrony-which is parent-of-origin dependent and lost upon neural differentiation-at the Dlk1-Dio3 and Snrpn domains, which both comprise lncRNA polycistrons. Generating a range of mutant lines, we find that asynchronous replication at Dlk1-Dio3 is mediated by differential DNA methylation, and that the lncRNA Meg3 controls early replication across parts of the domain on the maternal chromosome. Moreover, we find no evidence that RT and organisation into TADs are linked in this domain. The combined replication timing, DNA methylation, 3D chromatin structure, and gene expression data highlight how parental methylation imprints and lncRNA expression control replication and can override RT domain organisation.
Read more19Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information
BackgroundIntra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs). Here we propose a novel method (Deep-ITH) to predict and measure ITH, and we evaluate its prognostic performance in breast cancer. MethodsDeep convolutional neural networks were used to spatially predict gene-expression (PAM50 set) from WSIs. For each predicted transcript, 12 measures of heterogeneity were extracted in the training data set (N = 931). A prognostic score to dichotomise patients into Deep-ITH low- and high-risk groups was established using an elastic-net regularised Cox proportional hazards model (recurrence-free survival). Prognostic performance was evaluated in two independent data sets: SöS-BC-1 (N = 1358) and SCAN-B-Lund (N = 1262). ResultsWe observed an increase in risk of recurrence in the high-risk group with hazard ratio (HR) 2.11 (95%CI:1.22–3.60; p = 0.007) using nested cross-validation. Subgroup analyses confirmed the prognostic performance in oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, grade 3, and large tumour subgroups. The prognostic value was confirmed in the independent SöS-BC-1 cohort (HR=1.84; 95%CI:1.03–3.3; p = 3.99 ×10−2). In the other external cohort, significant HR was observed in the subgroup of histological grade 2 patients, as well as in the subgroup of patients with small tumours (<20 mm). ConclusionWe developed a novel method for an automated, scalable, and cost-efficient measure of ITH from WSIs that provides independent prognostic value for breast cancer. SignificanceTranscriptional ITH predicted by deep learning models enables prediction of patient survival from routine histopathology WSIs in breast cancer.
Read more147Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study
BackgroundTargeted therapy and immunotherapy put forward higher demands for accurate lung cancer classification, as well as benign versus malignant disease discrimination. Digital whole slide images (WSIs) witnessed the transition from traditional histopathology to computational approaches, arousing a hype of deep learning methods for histopathological analysis. We aimed at exploring the potential of deep learning models in the identification of lung cancer subtypes and cancer mimics from WSIs.MethodsWe initially obtained 741 WSIs from the First Affiliated Hospital of Sun Yat-sen University (SYSUFH) for the deep learning model development, optimization, and verification. Additional 318 WSIs from SYSUFH, 212 from Shenzhen People’s Hospital, and 422 from The Cancer Genome Atlas were further collected for multi-centre verification. EfficientNet-B5- and ResNet-50-based deep learning methods were developed and compared using the metrics of recall, precision, F1-score, and areas under the curve (AUCs). A threshold-based tumour-first aggregation approach was proposed and implemented for the label inferencing of WSIs with complex tissue components. Four pathologists of different levels from SYSUFH reviewed all the testing slides blindly, and the diagnosing results were used for quantitative comparisons with the best performing deep learning model.ResultsWe developed the first deep learning-based six-type classifier for histopathological WSI classification of lung adenocarcinoma, lung squamous cell carcinoma, small cell lung carcinoma, pulmonary tuberculosis, organizing pneumonia, and normal lung. The EfficientNet-B5-based model outperformed ResNet-50 and was selected as the backbone in the classifier. Tested on 1067 slides from four cohorts of different medical centres, AUCs of 0.970, 0.918, 0.963, and 0.978 were achieved, respectively. The classifier achieved high consistence to the ground truth and attending pathologists with high intraclass correlation coefficients over 0.873.ConclusionsMulti-cohort testing demonstrated our six-type classifier achieved consistent and comparable performance to experienced pathologists and gained advantages over other existing computational methods. The visualization of prediction heatmap improved the model interpretability intuitively. The classifier with the threshold-based tumour-first label inferencing method exhibited excellent accuracy and feasibility in classifying lung cancers and confused nonneoplastic tissues, indicating that deep learning can resolve complex multi-class tissue classification that conforms to real-world histopathological scenarios.
Read moreRisk stratification using the Decipher 22-gene genomic classifier (GC) and digital pathology artificial intelligence (DPAI) in nearly 10,000 localized prostate cancer patients.
408 Background: DPAI models have recently demonstrated the potential to improve risk stratification beyond routine clinical and pathologic variables. However, it is not known whether integrating DPAI information will enhance the prognostic accuracy of validated gene expression tests. In this study, the prognostic performance of novel DPAI algorithms were assessed in context to the genomic classifier (GC). Methods: A prospectively collected cohort of 9,874 patients with localized prostate cancer was retrieved from the Veracyte GRID registry (NCT02609269). Scans at 40X magnification were obtained for 17,701 H&E slides using an Aperio GT450 scanner (Leica). An open-source whole-slide pathology foundation model (GigaPath) was used to encode whole slide image (WSI) patches into digital pathology image features (DPIF). Attention-based multiple instance learning approach was used to develop separate models to predict distant metastasis (DM) in the biopsy and radical prostatectomy (RP) training subsets. WSI, baseline clinical variables, and GC scores were linked using tokenization (Datavant) to real-world data (Clarivate). The primary endpoint of this study was DM. Adjusted Hazard ratio (aHR) from multivariable Cox regression (MVA) modeling and 5-year area-under curve (AUC) estimates were used to compare models using DPIF. Results: Median follow-up for the training cohort (n=6,705; 239 DM events) and validation (n=3,169; 110 DM events) cohorts were 6.5 years. In the biopsy validation cohort of 999 patients, the GC predicted DM with an AUC of 0.80 (95% CI, 0.70-0.90), which exceeded NCCN risk group alone (AUC 0.68, 0.56-0.80) and DPAI (AUC 0.76, 0.66-0.86). MVA including age, NCCN, GC score, and DPAI, only showed GC (aHR 1.23 [1.03, 1.47]) and DPAI (aHR 1.22 [1.04, 1.43]) to be significantly associated with risk of DM (aHR per 10%, both p<0.05). However, an integrated model with clinicopathologic features, DPAI, and GC did not improve discrimination (AUC 0.80, 0.70-0.90). In the RP validation cohort of 1,492 patients, GC predicted DM with an AUC of 0.81 (95% CI, 0.73-0.88). An integrated model with clinicopathologic features, DPAI, and GC improved the AUC to 0.84 (0.77- 0.91). In MVA, only RP GC (aHR 1.27 [1.13, 1.43]) and DPAI (aHR 1.44 [1.23, 1.69]) were significant predictors for DM (both p<0.001). Conclusions: To our knowledge, this is the largest study assessing the prognostic value of adding DPAI to improve performance above and beyond a clinical-genomic model. The results from this study suggest that the combination of these data sources may further improve prognostication, and use of both DPAI and genomics negated information from routine clinical variables. Ongoing efforts in larger cohorts are underway to identify optimal scenarios in which GC and DPAI information enhance clinical utility for decision making.
Read more7AI in NSCLC: PET-CT & histology model.
e21044 Background: Almost 70% of cases of lung cancer are diagnosed at advanced stage, albeit with sophisticated imaging modalities available. Of these, 35% are tiny nodules that are often missed at initial radiological screening, owing to limitations of resolution of human vision.Dramatic shift in therapeutic paradigm in the management of lung cancer in the last decade has ushered an era of stratified medicine. In India, one-third population inhabits rural lands;lack of awareness for molecular medicine poses challenges to treating oncologists. AI-based cancer detection and prediction of potential oncogenic driver at diagnosis, will help alleviate patient anxiety caused due to diagnostic delays, aiding in prompt initiation of optimal therapy.Here, a comprehensive deep-CNN based diagnosis of lung adenocarcinoma (LUAD) was implemented using both PET/CT and histopathology images of 100 patients. Methods: Data collection: 143 whole-slide images were downloaded from Dartmouth Lung Cancer Histology Dataset from Biomedical Informatics Research and Data Science website and PET/CT dataset-A Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis (Lung-PET-CT-Dx) from The Cancer Imaging Archive. This comprises 251,135 images from 355 participants across 436 studies. Creation of Deep-CNN model: Residual Neural Network - 18 was used for histopathlogic classification followed by Detectron2’s region-based deep-CNN for detection of presence of tumours in lung PET/CT images. Properties such as size, morphology, attenuation, malignancy, and spiculation were also measured. The 18-layer deep network, ResNet-18, detected the histologic patterns using whole-slide histopathology images. Results: Results for 100 patients whose PET/CT images, histopathology images, and mutational status of three genes (EGFR, ALK, ROS) were readily available. The histologic patterns of tumour cells appear to be related to the mutational status of genes. The ResNet-18 CNN could predict various histologic patterns (Acinar, Lepidic, Papillary, Micropapillary, and Solid patterns). Conclusions: CNN models can help in early diagnosis and prompt treatment of lung cancer, if fairly accurate. Larger datasets are needed to improve the same. The final validation of the model is underway and will be reported in near future.[Table: see text]
Read more9Enhancing Weakly Supervised Semantic Segmentation With Multi-Label Contrastive Learning and LLM Features Guidance.
Histopathological whole-slide images (WSIs) segmentation is essential for precise tissue characterization in medical diagnostics. However, traditional approaches require labor-intensive pixel-level annotations. To this end, we study weakly supervised semantic segmentation (WSSS) which uses patch-level classification labels, reducing annotation efforts significantly. However, the complexity of WSIs and the challenge of sparse classification labels hinder effective dense pixel predictions. Moreover, due to the multi-label nature of WSI, existing approaches of single-label contrastive learning designed for the representation of single-category, neglecting the presence of other relevant categories and thus fail to adapt to WSI tasks. This paper presents a novel multi-label contrastive learning method for WSSS by incorporating class-specific embedding extraction with LLM features guidance. Specifically, we propose to obtain class-specific embeddings by utilizing classifier weights, followed by a dot-product-based attention fusion method that leverages LLM features to enrich their semantics, facilitating contrastive learning between different classes from single image. Besides, we propose a Robust Learning approach that leverages multi-layer features to evaluate the uncertainty of pseudo-labels, thereby mitigating the impact of noisy pseudo-labels on the learning process of segmentation. Extensive experiments have been conducted on two histopathological image segmentation datasets, i.e. LUAD dataset and BCSS dataset, demonstrating the effectiveness of our methods with leading performance.
Read more2Deep-learning analysis of H&E images to define three immune phenotypes to reveal loss-of-target in excluded immune cells as a novel resistance mechanism of immune checkpoint inhibitor in non-small cell lung cancer.
3120 Background: Discovery of predictive biomarker to enrich the responder of immune checkpoint inhibitor (ICI) in PD-L1-low ( < 50%) non-small cell lung cancer (NSCLC) is still challenging. Recent study showed that loss of heterozygosity (LOH) of HLA led to immune evasion. In the current study, we hypothesized that 3 immune phenotype (3IP): inflamed, excluded and desert would be reliably classified by deep-learning algorithm of H&E image, called Lunit-SCOPE, which would dictate the responder in PD-L1-low NSCLC patients and discover a unique resistance pathway in excluded phenotype. Methods: Lunit-SCOPE was trained with 1,824 H&E Whole-Slide Image (WSI) of NSCLC from Samsung Medical Center (SMC). WSI was divided into patches (~10 high-power fields) which was classified for 3IP, based on both quantity and localization of immune cells. The 3IP was trained and optimized by considering clinical outcome of 119 NSCLC patients with PD-(L)1 inhibitor (training cohort, patches = 25,897), and validated in 62 patients enrolled in LC-biomarker study (NCT03578185, validation cohort, patches = 8,929). Tumor Proportion Score (TPS) of PD-L1 22C3 immunohistochemistry was assessed by pathologists. Tumor Mutational Burden (TMB) was calculated as number of nonsynonymous alterations throughout whole-exome and HLA LOH was called by LOHHLA algorithm. Results: Interactive analysis to classify 3IP in training cohort showed that 8,726 (33.7%), 10,965 (42.3%), and 6,206 (24.0%) patches were classified as inflamed, excluded, and desert, respectively. In validation cohort, median progression-free survival (mPFS) of inflamed phenotype was 10.1 m, significantly prolonged compared to either excluded phenotype (3.0 m, P= 0.0053) or desert phenotype (1.4 m, P= 0.0011). Inflamed phenotype independently dictated favorable ICI outcome in PD-L1-low (TPS < 50%, mPFS of inflamed: 14.3 m vs excluded/desert: 1.4 m, P= 0.0233) as well as in PD-L1-high (TPS≥50%, 10.1 m vs 4.2 m, P= 0.0361), respectively. Excluded phenotype had higher TMB compared to inflamed phenotype had (median 177 vs 107), and HLA LOH was also enriched in excluded phenotype (31.0%) compared to inflamed (17.6%) and desert (16.7%) phenotypes. Conclusions: Lunit-SCOPE based 3IP classification can predict ICI outcome especially in PD-L1-low ( < 50%) patients. Excluded phenotype showed poor ICI outcome even with high TMB, partially explained by HLA LOH resulting in loss-of-target, as a novel resistance mechanism of ICI.
Read more
Copyright 2026 Cactus Communications. All rights reserved.
