Spatial Intratumoral Heterogeneity Research Articles

Defining and modeling dynamic spatial heterogeneity within tumor microenvironments

Many solid tumors exhibit significant genetic, cellular, and biophysical heterogeneity which dynamically evolves during disease progression and after treatment. This constant flux in cell composition, phenotype, spatial relationships, and tissue properties poses significant challenges in accurately diagnosing and treating patients. Much of the complexity lies in unraveling the molecular changes in different tumor compartments, how they influence one another in space and time and where vulnerabilities exist that might be appropriate to target therapeutically. Recent advances in spatial profiling tools and technologies are enabling new insight into the underlying biology of complex tumors, creating a greater understanding of the intricate relationship between cell types, states, and the microenvironment. Here we reflect on some recent discoveries in this area, where the key knowledge and technology gaps lie, and the advancements in spatial measurements and in vitro models for the study of spatial intratumoral heterogeneity.

Evaluating the immunologically “cold” tumor microenvironment after treatment with immune checkpoint inhibitors utilizing PET imaging of CD4 + and CD8 + T cells in breast cancer mouse models

BackgroundImmune-positron emission tomography (PET) imaging with tracers that target CD8 and granzyme B has shown promise in predicting the therapeutic response following immune checkpoint blockade (ICB) in immunologically “hot” tumors. However, immune dynamics in the low T-cell infiltrating “cold” tumor immune microenvironment during ICB remain poorly understood. This study uses molecular imaging to evaluate changes in CD4 + T cells and CD8 + T cells during ICB in breast cancer models and examines biomarkers of response.Methods[89Zr]Zr-DFO-CD4 and [89Zr]Zr-DFO-CD8 radiotracers were used to quantify changes in intratumoral and splenic CD4 T cells and CD8 T cells in response to ICB treatment in 4T1 and MMTV-HER2 mouse models, which represent immunologically “cold” tumors. A correlation between PET quantification metrics and long-term anti-tumor response was observed. Further biological validation was obtained by autoradiography and immunofluorescence.ResultsFollowing ICB treatment, an increase in the CD8-specific PET signal was observed within 6 days, and an increase in the CD4-specific PET signal was observed within 2 days in tumors that eventually responded to immunotherapy, while no significant differences in CD4 or CD8 were found at the baseline of treatment that differentiated responders from nonresponders. Furthermore, mice whose tumors responded to ICB had a lower CD8 PET signal in the spleen and a higher CD4 PET signal in the spleen compared to non-responders. Intratumoral spatial heterogeneity of the CD8 and CD4-specific PET signals was lower in responders compared to non-responders. Finally, PET imaging, autoradiography, and immunofluorescence signals were correlated when comparing in vivo imaging to ex vivo validations.ConclusionsCD4- and CD8-specific immuno-PET imaging can be used to characterize the in vivo distribution of CD4 + and CD8 + T cells in response to immune checkpoint blockade. Imaging metrics that describe the overall levels and distribution of CD8 + T cells and CD4 + T cells can provide insight into immunological alterations, predict biomarkers of response to immunotherapy, and guide clinical decision-making in those tumors where the kinetics of the response differ.

Spatial Transcriptome-Wide Profiling of Small Cell Lung Cancer Reveals Intra-Tumoral Molecular and Subtype Heterogeneity.

Small cell lung cancer (SCLC) is a highly aggressive malignancy characterized by rapid growth and early metastasis and is susceptible to treatment resistance and recurrence. Understanding the intra-tumoral spatial heterogeneity in SCLC is crucial for improving patient outcomes and clinically relevant subtyping. In this study, a spatial whole transcriptome-wide analysis of 25 SCLC patients at sub-histological resolution using GeoMx Digital Spatial Profiling technology is performed. This analysis deciphered intra-tumoral multi-regional heterogeneity, characterized by distinct molecular profiles, biological functions, immune features, and molecular subtypes within spatially localized histological regions. Connections between different transcript-defined intra-tumoral phenotypes and their impact on patient survival and therapeutic response are also established. Finally, a gene signature, termed ITHtyper, based on the prevalence of intra-tumoral heterogeneity levels, which enables patient risk stratification from bulk RNA-seq profiles is identified. The prognostic value of ITHtyper is rigorously validated in independent multicenter patient cohorts. This study introduces a preliminary tumor-centric, regionally targeted spatial transcriptome resource that sheds light on previously unexplored intra-tumoral spatial heterogeneity in SCLC. These findings hold promise to improve tumor reclassification and facilitate the development of personalized treatments for SCLC patients.

RE: Spatial intratumor heterogeneity of programmed cell death ligand 1 expression predicts poor prognosis in resected non–small cell lung cancer

RE: Spatial intratumor heterogeneity of programmed cell death ligand 1 expression predicts poor prognosis in resected non–small cell lung cancer

Resolving spatial response heterogeneity in glioblastoma.

Spatial intratumoral heterogeneity poses a significant challenge for accurate response assessment in glioblastoma. Multimodal imaging coupled with advanced image analysis has the potential to unravel this response heterogeneity. Based on automated tumor segmentation and longitudinal registration with follow-up imaging, we categorized contrast-enhancing voxels of 61 patients with suspected recurrence of glioblastoma into either true tumor progression (TP) or pseudoprogression (PsP). To allow the unbiased analysis of semantically related image regions, adjacent voxels with similar values of cerebral blood volume (CBV), FET-PET, and contrast-enhanced T1w were automatically grouped into supervoxels. We then extracted first-order statistics as well as texture features from each supervoxel. With these features, a Random Forest classifier was trained and validated employing a 10-fold cross-validation scheme. For model evaluation, the area under the receiver operating curve, as well as classification performance metrics were calculated. Our image analysis pipeline enabled reliable spatial assessment of tumor response. The predictive model reached an accuracy of 80.0% and a macro-weighted AUC of 0.875, which takes class imbalance into account, in the hold-out samples from cross-validation on supervoxel level. Analysis of feature importances confirmed the significant role of FET-PET-derived features. Accordingly, TP- and PsP-labeled supervoxels differed significantly in their 10th and 90th percentile, as well as the median of tumor-to-background normalized FET-PET. However, CBV- and T1c-related features also relevantly contributed to the model's performance. Disentangling the intratumoral heterogeneity in glioblastoma holds immense promise for advancing precise local response evaluation and thereby also informing more personalized and localized treatment strategies in the future.

Pathologic response rates to neoadjuvant pembrolizumab in locally advanced (LA) resectable cutaneous squamous cell carcinoma (cSCC).

9591 Background: Cutaneous squamous cell carcinoma (cSCC) is the second commonest cutaneous malignancy, accounting for 20% of cutaneous malignancies and 75% of non-melanoma skin cancer deaths. Anti-PD-1 is approved in unresectable and metastatic cSCC, and is associated with high rates of pathologic response in resectable locally advanced (LA) cSCC. We conducted a phase II single-arm neoadjuvant trial of pembrolizumab (P) in patients with PD-1 naïve resectable LA cSCC. Methods: Patients with AJCC/UICC ≥T3 (or T2 with ≥2 BWH high-risk features) and/or N+ disease were eligible. Patients received 2 cycles of P (200mg Q3W) prior to definitive surgery, and 15 cycles post-surgery (NCT04808999). The primary endpoint was pathologic complete response (pCR, defined as 0% residual viable tumor, RVT) per independent central pathology review. Key secondary endpoints included incidence of adverse events (AEs), recurrence-free survival (RFS), overall survival (OS), safety and unbiased spatial biomarkers. A selected panel of multiplex immunofluorescence imaging biomarkers were used by PredxBio Inc.’s computational pipeline to reveal spatial intratumor heterogeneity in the tumor microenvironment (TME). Results: Thirty patients were enrolled and received at least 1 cycle of P. The median age was 79 (59-93) years and patients were predominantly male (77%). One patient withdrew consent prior to surgery. Three patients died prior to definitive surgery: 2 from COVID-19, a third from a NSTEMI possibly related to P. Of the 26 response-evaluable patients, AJCC/UICC stage at baseline was high-risk T2, T3, N1, and N2 in 2 (8%), 12 (46%), 9 (34.5%), and 3 (11.5%) patients, respectively. We observed pCR in 15 (57%), pathologic partial response (pPR, 10% < %RVT <50%) in 2 (8%) and pathologic non-response (pNR, >50%RVT) in 8 (31%) patients. One patient is pending surgery. Postoperative radiotherapy (RT) was de-escalated in 54% of the patients (14/15 in pCR). Median RFS was 13 (pCR) versus 10.5 (non-pCR) months. One Grade 5 (NSTEMI) and two Grade 3 (hepatitis, colitis) treatment-related AEs were observed. Computational analysis identifies key spatial interactions between PDL-1+/CD68+ cells implicated in response to P. Conclusions: Neoadjuvant P is efficacious in resectable LA cSCC with a high pCR rate (57%). Three deaths were observed, including 2 unrelated to treatment and 1 possible Grade 5 cardiac irAE, reflecting the inherent risks of perioperative therapy in this patient population. RT was de-escalated in 93% of pCR patients. No relapse events in pCR patients. Spatial interactions between PDL-1+ cells and CD68+ macrophages have a role in therapeutic response and will be detailed along with other spatial analyses of the TME compartment that have an impact on outcomes. Clinical trial information: NCT04808999 .

Spatially resolved tissue imaging to analyze the tumor immune microenvironment: beyond cell-type densities

IntroductionThe tissue immune microenvironment is associated with key aspects of tumor biology. The interaction between the immune system and cancer cells has predictive and prognostic potential across different tumor types....

Tumor phylogeography reveals block-shaped spatial heterogeneity and the mode of evolution in Hepatocellular Carcinoma

Solid tumors are complex ecosystems with heterogeneous 3D structures, but the spatial intra-tumor heterogeneity (sITH) at the macroscopic (i.e., whole tumor) level is under-explored. Using a phylogeographic approach, we sequence genomes and transcriptomes from 235 spatially informed sectors across 13 hepatocellular carcinomas (HCC), generating one of the largest datasets for studying sITH. We find that tumor heterogeneity in HCC segregates into spatially variegated blocks with large genotypic and phenotypic differences. By dissecting the transcriptomic heterogeneity, we discover that 30% of patients had a “spatially competing distribution” (SCD), where different spatial blocks have distinct transcriptomic subtypes co-existing within a tumor, capturing the critical transition period in disease progression. Interestingly, the tumor regions with more advanced transcriptomic subtypes (e.g., higher cell cycle) often take clonal dominance with a wider geographic range, rejecting neutral evolution for SCD patients. Extending the statistical tests for detecting natural selection to many non-SCD patients reveal varying levels of selective signal across different tumors, implying that many evolutionary forces including natural selection and geographic isolation can influence the overall pattern of sITH. Taken together, tumor phylogeography unravels a dynamic landscape of sITH, pinpointing important evolutionary and clinical consequences of spatial heterogeneity in cancer.

Abstract 1160: Investigating spatial gene expression profiling in colorectal cancer: Tumor and stroma comparison between primary lesions and matched liver metastases

Abstract The nanoString GeoMx® Digital Spatial Profiling (DSP) enables the investigation of spatial assessment of tumors through high-plex profiling at the RNA and protein levels. Recent genomic analyses have revealed the intertumor heterogeneity between primary and metastatic lesions in colorectal cancer (CRC) patients. In CRC with liver metastases, current treatment strategies are mainly based on the parameters of primary tumors, and metastatic heterogeneity is a challenge since molecular heterogeneity contributes to therapeutic resistance. Furthermore, spatial intratumor heterogeneity exists within a single tumor between cancer cells (tumor) and their microenvironment (stroma) in human cancers. However, whether there are distinct spatial gene expression patterns of tumor and stroma between primary lesions and liver metastases remains unclear in CRCs. We examined 24 formalin-fixed paraffin-embedded tissue samples, including primary lesions and matched liver metastases from 12 patients (6 with synchronous and 6 with metachronous metastases) using NanoString GeoMx® DSP. The best regions of interest (ROI) with the invasive front boundary of the tumor were selected by pathologists. The ROI was segmented into PanCK-positive (tumor) and PanCK-negative (stroma), followed by a collection of indexed oligonucleotides and sequencing on Illumina instrument. Differential expression and pathways enrichment analyses were performed using R BioConductor package standR, limma, and GSEABase. Statistical significances were based on |Log2 fold change| > 1 and Benjamini-Hochberg-corrected P < 0.05. Immune cell abundance was estimated using the SpatialDecon package. In 12 metastatic patients (mean age, 61 years ± 7 [standard deviation]), liver metastatic stroma was associated with 68 upregulated and 93 downregulated genes compared to the primary stroma, with enrichment of 'Immune response' terms in gene set enrichment analysis (GSEA). A higher proportion of CD4 T memory cells and increased expression of cytotoxic genes (IL7R [2.02-fold], CD3E [1.75-fold], KLRB1 [1.68-fold], and GZMK [1.54-fold]) were observed in liver stroma compared to primary stroma. Conversely, liver metastatic tumors were associated only with 23 upregulated and 6 downregulated genes compared to primary tumors, with 'humoral response' terms enriched in GSEA. Similar patterns were observed in stratified analyses with synchronous and metachronous metastases, although a more prominent ‘immune response’ was observed in synchronous metastatic stroma compared to the primary stroma. We revealed that higher immune cell proportion and cytotoxic activity were observed in liver metastatic stroma compared to CRC stroma, providing novel insights into a unique etiology and may yield clinical implications for developing targeted treatment modalities for liver metastatic CRC patients. Citation Format: Jongwon Lee, Yeseul Kim, Hyo Seon Ryu, Jongmin Sim, Chungyeul Kim, Jong Min Park, Ah-Reum Lim, Jung Sun Kim, Hwa Jung Sung, Xingyi Guo, Jungmin Choi, Jungyoon Choi. Investigating spatial gene expression profiling in colorectal cancer: Tumor and stroma comparison between primary lesions and matched liver metastases [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 1160.

Spatial intratumor heterogeneity of programmed death-ligand 1 expression predicts poor prognosis in resected non-small cell lung cancer.

We quantified the pathological spatial intratumor heterogeneity of programmed death-ligand 1 (PD-L1) expression and investigated its relevance to patient outcomes in surgically resected non-small cell lung carcinoma (NSCLC). This study enrolled 239 consecutive surgically resected NSCLC specimens of pathological stage IIA-IIIB. To characterize the spatial intratumor heterogeneity of PD-L1 expression in NSCLC tissues, we developed a mathematical model based on texture image analysis and determined the spatial heterogeneity index of PD-L1 for each tumor. The correlation between the spatial heterogeneity index of PD-L1 values and clinicopathological characteristics, including prognosis, was analyzed. Furthermore, an independent cohort of 70 cases was analyzed for model validation. Clinicopathological analysis showed correlations between high spatial heterogeneity index of PD-L1 values and histological subtype (squamous cell carcinoma; P < .001) and vascular invasion (P = .004). Survival analysis revealed that patients with high spatial heterogeneity index of PD-L1 values presented a significantly worse recurrence-free rate than those with low spatial heterogeneity index of PD-L1 values (5-year recurrence-free survival [RFS] = 26.3% vs 47.1%, P < .005). The impact of spatial heterogeneity index of PD-L1 on cancer survival rates was verified through validation in an independent cohort. Additionally, high spatial heterogeneity index of PD-L1 values were associated with tumor recurrence in squamous cell carcinoma (5-year RFS = 29.2% vs 52.8%, P < .05) and adenocarcinoma (5-year RFS = 19.6% vs 43.0%, P < .01). Moreover, we demonstrated that a high spatial heterogeneity index of PD-L1 value was an independent risk factor for tumor recurrence. We presented an image analysis model to quantify the spatial intratumor heterogeneity of protein expression in tumor tissues. This model demonstrated that the spatial intratumor heterogeneity of PD-L1 expression in surgically resected NSCLC predicts poor patient outcomes.

AI-based intra-tumor heterogeneity score of Ki67 expression as a prognostic marker for early-stage ER+/HER2- breast cancer.

Early-stage estrogen receptor positive and human epidermal growth factor receptor negative (ER+/HER2-) luminal breast cancer (BC) is quite heterogeneous and accounts for about 70% of all BCs. Ki67 is a proliferation marker that has a significant prognostic value in luminal BC despite the challenges in its assessment. There is increasing evidence that spatial colocalization, which measures the evenness of different types of cells, is clinically important in several types of cancer. However, reproducible quantification of intra-tumor spatial heterogeneity remains largely unexplored. We propose an automated pipeline for prognostication of luminal BC based on the analysis of spatial distribution of Ki67 expression in tumor cells using a large well-characterized cohort (n = 2,081). The proposed Ki67 colocalization (Ki67CL) score can stratify ER+/HER2- BC patients with high significance in terms of BC-specific survival (p < 0.00001) and distant metastasis-free survival (p = 0.0048). Ki67CL score is shown to be highly significant compared with the standard Ki67 index. In addition, we show that the proposed Ki67CL score can help identify luminal BC patients who can potentially benefit from adjuvant chemotherapy.

Intratumor spatial heterogeneity in programmed death-ligand 1 (PD-L1) protein expression in early-stage breast cancer.

Programmed death-ligand 1 (PD-L1) expression is required for benefit from immune checkpoint inhibitors in metastatic triple negative breast cancer (TNBC). In contrast, in the neoadjuvant setting patients benefited regardless of PD-L1 expression. We hypothesized that, in stages II-III breast cancers, low levels of PD-L1 expression may be sufficient to confer sensitivity to therapy and focal expression could be missed by a biopsy. In this study, we examined intratumor spatial heterogeneity of PD-L1 protein expression in multiple biopsies from different regions of breast cancers in 57 primary breast tumors (n = 33 TNBC, n = 19 estrogen receptor-positive [ER-positive], n = 5 human epidermal receptor 2-positive [HER2 +]). E1L3N antibody was used to assess PD-L1 status and staining was scored using the combined positivity score (CPS) with PD-L1 positive defined as CPS ≥ 10. Overall, 19% (11/57) of tumors were PD-L1 positive based on positivity in at least 1 biopsy. Among TNBC, PD-L1 positivity was 27% (9/33). The discordance rate, defined as the same tumor yielding PD-L1 positive and negative samples in different regions, was 16% (n = 9) in the whole study population and 23% (n = 7) in TNBC. Cohen's kappa coefficient of agreement was 0.214 for the whole study and 0.239 for TNBC, both of which falling into a non-statistically significant fair agreement range. Among all PD-L1 positive cases, 82% (n = 9/11) had positivity in only one of the tissue assessments. These results indicate that the overall 84% concordance is driven by concordant negative results. In PD-L1 positive cancers, within-tumor heterogeneity in PD-L1 expression exists.

Prognostic value of tumor necrosis based on the evaluation of frequency in invasive breast cancer

BackgroundTumor necrosis (TN) was associated with poor prognosis. However, the traditional classification of TN ignored spatial intratumor heterogeneity, which may be associated with important prognosis. The purpose of this study was to propose a new method to reveal the hidden prognostic value of spatial heterogeneity of TN in invasive breast cancer (IBC).MethodsMultiphoton microscopy (MPM) was used to obtain multiphoton images from 471 patients. According to the relative spatial positions of TN, tumor cells, collagen fibers and myoepithelium, four spatial heterogeneities of TN (TN1-4) were defined. Based on the frequency of individual TN, TN-score was obtained to investigate the prognostic value of TN.ResultsPatients with high-risk TN had worse 5-year disease-free survival (DFS) than patients with no necrosis (32.5% vs. 64.7%; P < 0.0001 in training set; 45.8% vs. 70.8%; P = 0.017 in validation set), while patients with low-risk TN had a 5-year DFS comparable to patients with no necrosis (60.0% vs. 64.7%; P = 0.497 in training set; 59.8% vs. 70.8%; P = 0.121 in validation set). Furthermore, high-risk TN “up-staged” the patients with IBC. Patients with high-risk TN and stage I tumors had a 5-year DFS comparable to patients with stage II tumors (55.6% vs. 62.0%; P = 0.565 in training set; 62.5% vs. 66.3%; P = 0.856 in validation set), as well as patients with high-risk TN and stage II tumors had a 5-year DFS comparable to patients with stage III tumors (33.3% vs. 24.6%; P = 0.271 in training set; 44.4% vs. 39.3%; P = 0.519 in validation set).ConclusionsTN-score was an independent prognostic factor for 5-year DFS. Only high-risk TN was associated with poor prognosis. High-risk TN “up-staged” the patients with IBC. Incorporating TN-score into staging category could improve its performance to stratify patients.

Non-invasive classification of IDH mutation status of gliomas from multi-modal MRI using a 3D convolutional neural network.

Glioma is the most common form of brain tumor with a high degree of heterogeneity in imaging characteristics, treatment-response, and survival rate. An important factor causing this heterogeneity is the mutation of isocitrate dehydrogenase (IDH) enzyme. The current clinical gold-standard for identifying IDH mutation status involves invasive procedures that involve risk, may fail to capture intra-tumoral spatial heterogeneity or can be inaccessible in low-resource settings. In this study, we propose a deep learning-based method to non-invasively and pre-operatively determine IDH status of high- and low-grade gliomas by leveraging their phenotypical characteristics from volumetric MRI scans. For this purpose, we propose a 3D Mask R-CNN-based approach to simultaneously detect and segment glioma as well as classify its IDH status - thus obviating the requirement of any separate tumor segmentation step. The network can operate on routinely acquired MRI sequences and is agnostic to glioma grade. It was trained on patient-cases from publicly available datasets (n = 223) and tested on two hold-out datasets acquired from The Cancer Genome Atlas (TCGA; n = 62) and Washington University School of Medicine (WUSM; n = 261). The model achieved areas under the receiver operating characteristic of 0.83 and 0.87, and areas under the precision-recall curves of 0.78 and 0.79, on the TCGA and WUSM sets, respectively. The model can be used to perform a pre-operative 'virtual biopsy' of gliomas, thus facilitating treatment planning, potentially leading to better overall survival.

Abstract 5445: Segmentation-free analysis of multiplexed images with unbiased spatial analytics and explainable AI for predicting disease outcomes

Abstract Background: The spatial intratumor heterogeneity (ITH) is widely acknowledged as driving therapeutic response and providing fuel for drug resistance. Currently, patient selection for immunotherapy is driven mostly by PD-1/PD-L1 based IHC tests and mutational analysis. These oversimplified approaches fail to predict the risk of recurrence, therapeutic response and drug resistance with high accuracy. We hypothesize that functional responses of heterogeneous non-random spatial arrangements of tumor, stromal and immune cells in the tumor microenvironment are determined by distinct combinations of their internal states and spatial interactions within neighborhoods. The interactions generate distinct cell-cell communication networks forming functional spatial configurations of cells, termed microdomains, as organizational units of spatial ITH. Deriving the spatial networks within each microdomain with unbiased spatial analytics and the underlying network biology through explainable AI, is key to understanding tumor initiation, tumor progression, and response to therapy. Problem: There has been an explosion of spatial imaging technologies using immunofluorescence and/or mass spectrometry for intact tissues measuring protein expressions, DNA and RNA probes. To extract high-value knowledge from these multiplexed datasets, a key first step is to segment cells accurately. Despite decades of research, this step remains elusive due to imaging artifacts arising from issues with tile stitching, incorrect registration, signal oversaturation, non-uniform illumination, poor or high-background nuclei signal, defocus, varying cell and nuclear shapes, fluorescence emission efficiency variation, overlapping and/or superimposed nuclei and low resolution (e.g., mass cytometry). Those artifacts may lead to incorrect cell phenotypes, incomplete cell phenotype atlases, and missing rare cell, fusion cell or transition cell types. Solution: We present TumorMapr™, a segmentation-free, unbiased spatial analytics and explainable AI platform that extracts information and creates knowledge from patient disease pathology samples imaged with any technology including fluorescence, mass spectrometry etc. Results: We apply TumorMapr on hyperplexed immunofluorescence dataset of colorectal cancer and hyperplexed imaging mass cytometry dataset of triple negative breast cancer to discover functional cell prototypes involving spatial collections of neighboring pixels highly predictive of disease progression and response to therapy, tumor promoting and tumor restraining microdomains and microdomain-specific network biology predictive of disease outcomes. We further compare these results with segmentation-based approaches to showcase the discovery of novel cell prototypes. Citation Format: Filippo Pullara, Brian Falkenstein, Bruce Campbell, Samantha Panakkal, Akif Burak Tosun, Jeffrey Fine, S. Chakra Chennubhotla. Segmentation-free analysis of multiplexed images with unbiased spatial analytics and explainable AI for predicting disease outcomes. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5445.

Abstract 2132: Tumor heterogeneity of CCNE1 copy number assessed by fluorescence in situ hybridization (FISH) in ovarian and uterine cancers and correlation with cyclin E protein expression

Abstract Amplification of CCNE1 is a recurrent genetic alteration that has been associated with chemoresistance and poor prognosis in gynecological malignancies. We identified Protein Kinase, Membrane Associated Tyrosine/Threonine 1 (PKMYT1) as a novel synthetic lethal target in CCNE1 amplified cancers. In this genomic setting, PKMYT1 inhibition causes premature mitotic entry, mitotic catastrophe and, in turn, cell death. In pre-clinical models, correlation between CCNE1 copy number, cyclin E1 expression and sensitivity to PKMYT1 inhibition is imperfect. Thus, a deep understanding of CCNE1 amplification as a predictive biomarker for PKMYT1 inhibition is critical for refining patient selection strategies and strengthening our understanding of the mechanism of action. These learnings will be applied to correlative biomarker analyses in clinical trials evaluating RP-6306, a first-in-class, potent, and highly selective PKMYT1 inhibitor, in tumors with CCNE1 amplifications (NCT04855656, NCT05147272, NCT05147350). In this study, multiple features of CCNE1 were examined using a combination of whole genome and targeted next generation sequencing (NGS), FISH, and immunohistochemistry (IHC). Cyclin E1 levels were evaluated across tumor tissue microarrays spanning 5 tumor indications: triple-negative breast (n=100), colorectal (n=100), gastric (n=100), uterine (n=49), ovarian (n=100) cancers. The highest cyclin E1 protein levels were found in ovarian and uterine malignancies, consistent with indications with high prevalence of CCNE1 amplification (median H-score ovarian = 125; uterine = 100; gastric = 45; TNBC = 48 and CRC = 24). To explore CCNE1 amplification on a single cell level, we established a novel FISH assay using a subtelomeric q-arm control probe. The subtelomeric q-arm band is more frequently diploid than other regions on chromosome 19. Analytical validation of the FISH assay demonstrated sensitivity of 95%, specificity of 95.2%, and overall concordance of 95.1% when compared to a targeted sequencing panel (SNIPDx®). Within the uterine and ovarian cohorts, cancers harboring CCNE1 amplification were found to display higher cyclin E1 protein levels than non-amplified tumors (p-val=0.0002) and cyclin E1 expression was positively correlated with CCNE1 copy number (Spearman ρ=0.67; p-value=4.1e-05). Substantial intratumoral spatial heterogeneity of CCNE1 copy number when measured by FISH was observed across ovarian and uterine tumors. The potential impact of CCNE1 amplification heterogeneity, cyclin E1 protein levels and CCNE1 amplification fragment size on response to RP-6306 in preclinical models are currently being investigated and will be reported. Citation Format: Adam Petrone, Elia Aguado-Fraile, Sunantha Sethuraman, Adrienne Johnson, Ian Silverman, Gary Marshall, Jorge S. Reis-Filho, John Iafrate, Artur Veloso, Victoria Rimkunas. Tumor heterogeneity of CCNE1 copy number assessed by fluorescence in situ hybridization (FISH) in ovarian and uterine cancers and correlation with cyclin E protein expression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2132.

Abstract 5934: Tracking T cell clonal dynamics across time and space in metastatic colorectal cancer

Abstract Here we tracked T cell dynamics in 15 metastatic colorectal cancer (mCRC) patients who had multiple resections (median: 3, range: 2-7) over a period of &amp;gt;7 years (median: 10 years, range: 7-16 years), using a novel T cell receptor sequencing (TCRseq) method. These samples had been previously characterized for DNA and methylation alterations and gene expression, enabling an integrated analysis to determine potential cellular and molecular drivers and consequences of immune dynamics. This multi-region, multi-timepoint dataset presents a unique opportunity to study the co-evolution of mCRC and the T cell response over time, across metastatic sites and in response to therapy. T cell antigen specificity is defined by the TCR - a highly diverse sequence that enables tracking of specific T cell expansions across time and space. TCRseq quantifies the abundance of T cell clones and maps the dynamics of the TCR repertoire, however little is known about how these are altered in different metastatic sites, and post-chemotherapy. We developed and validated a new FFPE-compatible TCRseq method and sequenced 216 longitudinal samples representing the mCRC cohort described above. We detected a median of 348 unique TCRs per sample (range 69-9700), and revealed high levels of intra-tumor spatial heterogeneity. No significant difference was detected in the number of T cell clones or the TCR clonality between primary tumors (n=40), lung metastases (n=30) and liver metastases (n=50). Furthermore, these were not significantly different between tumor regions (n=139) and surrounding stroma (n=38). We found that compared to chemo-naïve tumors (n=36), those that had been exposed to recent chemotherapy (n=41) had a significant increase in both the number of unique T cell clones and Simpson’s evenness, likely reflecting a broad T cell response to DNA-damaging agents. After more than a year had passed without chemotherapy (n=64) these returned to pre-chemotherapy levels. In most cases the 10 most abundant TCR sequences in the primary tumor represented 15-40% of the total repertoire. We tracked these expanded clones through metastases, identifying ubiquitous clones which persisted across metastatic sites and through therapy. We found other expanded clones which were no longer present in metastases lesions, although some of these were found to expand again in later metastases. Finally, we examined correlations with tumor genomics, finding evidence in some patients that the TCR repertoire tracks closely with tumor clones even through metastasis and chemotherapy. In summary, this project represents the first comprehensive analysis of T cell dynamics through colorectal cancer metastasis, highlighting the changing T cell landscape post-chemotherapy and correlating the TCR repertoire with tumor genomics. Our results could have important clinical implications for mCRC immunotherapy and the design of personalized T cell-based therapy. Citation Format: Ann-Marie Baker, Alison Berner, Tahel Ronel, Nick Trahearn, Barbara Bravi, John Bridgewater, Benny Chain, Trevor A. Graham. Tracking T cell clonal dynamics across time and space in metastatic colorectal cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5934.

Abstract 3363: Spatial and longitudinal tumor heterogeneity in head and neck squamous cell carcinoma patients treated with primary surgery

Abstract Introduction: Cell-free tumor DNA (ctDNA) is an emerging biomarker in head and neck squamous cell carcinoma (HNSCC) for disease staging, patients’ recurrence risk stratification and early detection of relapse. We aimed to compare variants identified in ctDNA versus surgical tumor specimen, and to study the evolution of the mutational landscape of ctDNA over time in HNSCC. Patients and Method: Forty-one HNSCC patients treated with curative-intent primary surgery from SCANDARE cohort (NCT03017573) were evaluated for longitudinal ctDNA-based NGS. Overall, 28 patients were treated with adjuvant (chemo)radiotherapy, and 31 experienced recurrence. Formalin-fixed paraffin-embedded tumor tissues at surgery were available for 41 patients. Serial contributive ctDNA were retrieved from all 41 patients at the date of surgery, 36 patients within 19 weeks after surgery, 20 patients at six months after surgery, and 22 patients at recurrence. Tissue DNA was personalized detected with a custom NGS panel of 571 genes (DRAGON) and ctDNA was sequenced using another personalized dedicated NGS panel including up to 15 genes (OncoFOLLOW). Results: Most frequently mutated genes in tissue included TP53 (15.9%), FAT1 (6.7%), NOTCH1 (5.5%) and PIK3CA (4.3%) with similar allelic ratio to ctDNA at baseline surgery. Higher prevalence of KRAS and TP53 mutations was found in ctDNA at recurrence in comparison with ctDNA and tissue, respectively, at baseline surgery (KRAS: 6.3% versus 1.6% and 0.6%; TP53: 31.2% versus 21.1% and 15.9%). Additional variants in NRAS, HRAS, TP53, JAK2 and SDHA were detected in 6 patients in ctDNA at surgery and were not found in tissue, suggesting spatial intratumor heterogeneity. Twenty-three/36 patients (64%) had detected ctDNA within 19 weeks after surgery among whom, 17/23 patients (74%) had disease recurrence. Eleven/20 patients (including 10 with adjuvant treatment) had detected ctDNA at six months after surgery among whom 6 patients (55%) had disease recurrence. Fifteen/22 patients (68%) had detected ctDNA at recurrence. Emerging pathogenic variants were found in patients with detected ctDNA after surgery (n=7/23; 30%), at six months after surgery (n=1/11; 9%) and at recurrence (n=4/15; 27%). Conclusion: Our study suggests spatial and longitudinal tumor heterogeneity and reports emerging mutations in ctDNA over time in HNSCC. Prognostic significance characterization of the ctDNA dominant clone allele frequency is ongoing. Citation Format: Grégoire Marret, Constance Lamy, Sophie Vacher, Mathieu Séné, Ladidi Ahmanache, Laura Courtois, Zakhia El Beiano, Jerzy Klijanienko, Charlotte Martinat, Nicolas Servant, Choumouss Kamoun, Linda Larbi Chérif, Thierry Bronzini, Cédric Balsat, Jean-François Laes, Aubray Prévot, Sébastien Sauvage, Maxime Lienard, Emmanuel Martin, Bérengère Genin, Nathalie Badois, Maria Lesnik, Antoine Dubray-Vautrin, Olivier Choussy, Wahib Ghanem, Rabah Taouachi, Julien Masliah Planchon, Ivan Bièche, Maud Kamal, Christophe Le Tourneau. Spatial and longitudinal tumor heterogeneity in head and neck squamous cell carcinoma patients treated with primary surgery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3363.

Mapping single-cell transcriptomes in the intra-tumoral and associated territories of kidney cancer.

Tumor behavior is intricately dependent on the oncogenic properties of cancer cells and their multi-cellular interactions. To understand these dependencies within the wider microenvironment, we studied over 270,000 single-cell transcriptomes and 100 microdissected whole exomes from 12 patients with kidney tumors, prior to validation using spatial transcriptomics. Tissues were sampled from multiple regions of the tumor core, the tumor-normal interface, normal surrounding tissues, and peripheral blood. We find that the tissue-type location of CD8+ Tcell clonotypes largely defines their exhaustion state with intra-tumoral spatial heterogeneity that is not well explained by somatic heterogeneity. De novo mutation calling from single-cell RNA-sequencing data allows us to broadly infer the clonality of stromal cells and lineage-trace myeloid cell development. We report six conserved meta-programs that distinguish tumor cell function, and find an epithelial-mesenchymal transition meta-program highly enriched at the tumor-normal interface that co-localizes with IL1B-expressing macrophages, offering a potential therapeutic target.

Intratumoral spatial heterogeneity of tumor-infiltrating lymphocytes is a significant factor for precisely stratifying prognostic immune subgroups of microsatellite instability-high colorectal carcinomas

Although the density of tumor-infiltrating lymphocytes (TILs) is known to be linked to prognosis in various cancers, the prognostic impact and immunologic significance of the spatial heterogeneity of TILs have been rarely investigated. In this study, CD3+ and CD8+ TILs were quantified in independent cohorts (discovery, n = 73; and external validation, n = 93) of colorectal carcinomas (CRCs) with microsatellite instability-high (MSI-H) utilizing whole-slide image analysis of CD3/CD8 immunohistochemistry. The Shannon and Simpson indices, which measure intratumoral patch-to-patch evenness of TIL densities, were used to quantitatively assess the spatial heterogeneity of TILs in each case. To uncover immune-related gene expression signatures of spatial heterogeneity-based TIL subgroups of MSI-H CRCs, representative cases were subjected to GeoMx digital spatial profiler (DSP) analysis. As expected, a low density of TILs was significantly associated with poor disease-free survival (DFS) in MSI-H CRCs. The TIL-low tumors were further classified into two subgroups based on the spatial heterogeneity of TILs: TIL-low/heterogeneity-high and TIL-low/heterogeneity-low subgroups. In both discovery and validation cohorts, the TIL-low/heterogeneity-high, TIL-low/heterogeneity-low, and TIL-high subgroups were significantly associated with poor, intermediate, and good DFS, respectively. In the DSP analysis, the TIL-low/heterogeneity-high subgroup showed higher spatial diversity in the expression of immune-related genes than that of the TIL-low/heterogeneity-low subgroup and exhibited upregulation of genes related to immune checkpoints, chemokine/cytokine receptors, and myeloid cells. TIL-low/heterogeneity-high tumors were also enriched with gene sets related to good response to immune checkpoint inhibitor therapy. In conclusion, TIL-low MSI-H CRCs are prognostically heterogeneous and can be divided into prognostically and immunologically distinct subgroups by considering the spatial heterogeneity of TILs. Our data suggest that intratumoral spatial heterogeneity of TILs can be used as a key element for clinically relevant immunologic subtyping of tumors.