A tumor microenvironment-based classification of gastric cancer for more effective diagnosis and treatment.

With approximately one million diagnosed cases and over 700,000 deaths recorded annually, gastric cancer (GC) is the third most common cause of cancer-related deaths worldwide. GC is a heterogeneous tumor. Thus, optimal management requires biomarkers of prognosis, treatment selection, and treatment response. The Cancer Genome Atlas program sub-classified GC into molecular subtypes, providing a framework for treatment personalization using traditional chemotherapies or biologics. Here, we report a comprehensive study of GC vascular and immune tumor microenvironment (TME)-based on stage and molecular subtypes of the disease and their correlation with outcomes. Using tissues and blood circulating biomarkers and a molecular classification, we identified cancer cell and tumor archetypes, which show that the TME evolves with the disease stage and is a major determinant of prognosis. Moreover, our TME-based subtyping strategy allowed the identification of archetype-specific prognostic biomarkers such as CDH1-mutant GC and circulating IL-6 that provided information beyond and independent of TMN staging, MSI status, and consensus molecular subtyping. The results show that integrating molecular subtyping with TME-specific biomarkers could contribute to improved patient prognostication and may provide a basis for treatment stratification, including for contemporary anti-angiogenesis and immunotherapy approaches.

Molecular subtypes of leiomyosarcoma: Moving toward a consensus

Background: Colorectal Cancer (CRC) can be classified into transcriptomics subtypes such as, stromal or immunogenic. A previously demonstrated consensus molecular subtype (CMS) classification method was utilized on CRC samples. We explored the potential clinical utility of combining a transcriptome derived subtypes approach with WES by demonstrating the association among subtypes and tumor characteristics such as whole exome based microsatellite instability (MSI), tumor mutational burden (TMB) and karyotype. Methods: Tumor tissue and paired blood samples were collected from 19 late-stage, treatment-naïve colorectal cancer (CRC) patients. gDNA and RNA were extracted and analyzed by the Personalis® ImmunoID NeXT Platform®. RNA-seq results were normalized with the R DESeq2 package, with CRC CMS classification performed by the R package CMScaller. Somatic variants, copy number variants and MSI were evaluated using paired tumor/normal (T/N) samples. Copy number (CN) was characterized both genome-wide as a ploidy estimate and focally as the number of amplified regions. TMB was computed in alignment with the Friends of Cancer Research phase I guidelines. Results: The most commonly mutated genes included TP53 (19/19), APC (16/19), and KRAS (9/19). CMScaller identified molecular subtypes (CMS1 immune/MSI n=1; CMS2 canonical/epithelial n=6; CMS3 metabolic/epithelial n=2; CMS4 mesenchyme/stromal n=8; unspecified n=2) in 17/19 samples. CMS2 and CMS4, the two most common molecular subtypes in this cohort, differed in terms of exome-wide MSI percent (p=0.03, Student’s t) and purity (p<0.007, Student’s t). TMB, in silico ploidy estimates, and focal copy number changes were assessed for associations with tumor purity, with a significant association only between purity and ploidy (p=0.014; Bonferroni q=0.42; Student’s t) at the highest and lowest purity quartiles. Exome-wide percent unstable MSI was significantly associated with tumor purity (p=0.0053, lowest vs. highest quartile purity), with high purity tumors possessing more MSI. No association between TMB and Union for International Cancer Control stage was identified. Conclusion: It has been demonstrated that the ImmunoID NeXT Platform’s transcriptomics capabilities enable CMS classification in most samples (17/19, 89%), an overall performance superior or comparable to previously reported results. An association between tumor purity and WES MSI using a broader exome-wide measurement was identified with transcriptomic based molecular subtyping. It has been corroborated that future comprehensive molecular classifiers can expand on transcriptomics based classification by leveraging DNA-based measurements to further delineate subtypes and eventually lead to biomarker driven precision oncology focused patient selection. Citation Format: Danyi Wang, Lee McDaniel, Sean Boyle, Juergen Scheuenpflug, Zheng Feng. Comprehensive next generation sequencing profiling in combination with transcriptomic-based tumor molecular subtyping and harmonized TMB calculation using paired specimens from late-stage CRC patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5743.

BackgroundMolecular subtypes predict prognosis in muscle-invasive bladder cancer (MIBC) and are explored as predictive markers. To provide a common base for molecular subtyping and facilitate clinical applications, a consensus classification has been developed. However, methods to determine consensus molecular subtypes require validation, particularly when FFPE specimens are used. Here, we aimed to evaluate two gene expression analysis methods on FFPE samples and to compare reduced gene sets to classify tumors into molecular subtypes.MethodsRNA was isolated from FFPE blocks of 15 MIBC patients. Massive analysis of 3’ cDNA ends (MACE) and the HTG transcriptome panel (HTP) were used to retrieve gene expression. We used normalized, log2-transformed data to call consensus and TCGA subtypes with the consensusMIBC package for R using all available genes, a 68-gene panel (ESSEN1), and a 48-gene panel (ESSEN2).ResultsFifteen MACE-samples and 14 HTP-samples were available for molecular subtyping. The 14 samples were classified as Ba/Sq in 7 (50%), LumP in 2 (14.3%), LumU in 1 (7.1%), LumNS in 1 (7.1%), stroma-rich in 2 (14.3%) and NE-like in 1 (7.1%) case based on MACE- or HTP-derived transcriptome data. Consensus subtypes were concordant in 71% (10/14) of cases when comparing MACE with HTP data. Four cases with aberrant subtypes had a stroma-rich molecular subtype with either method. The overlap of the molecular consensus subtypes with the reduced ESSEN1 and ESSEN2 panels were 86% and 100%, respectively, with HTP data and 86% with MACE data.ConclusionDetermination of consensus molecular subtypes of MIBC from FFPE samples is feasible using various RNA sequencing methods. Inconsistent classification mainly involves the stroma-rich molecular subtype, which may be the consequence of sample heterogeneity with (stroma)-cell sampling bias and highlights the limitations of bulk RNA-based subclassification. Classification is still reliable when analysis is reduced to selected genes.

Objective: Consensus molecular subtype (CMS) is a predictive factor for treatment outcomes of chemotherapies for metastatic colorectal cancer (CRC). CMS classification is based on transcriptomic profiles of CRC specimens obtained by tumor biopsy. Tumor biopsy often provides limited information due to the heterogeneity within tumor and spatial heterogeneity between the primary tumor and distant metastases. In addition, repeated tissue biopsy to monitor the treatment response is not practical. Liquid biopsy is a minimally invasive method for the real-time monitoring of cancer-derived biomarkers. Among liquid biopsy biomarkers, extracellular vesicles (EVs) have a unique potential because they possess nucleic acids. We explored the use of CRC plasma EVs in predicting the molecular subtype of CRCs using RNA-seq. Methods: EVs were isolated from 10 different CMS-stratified colorectal cancer cell lines and a normal control. Whole transcriptome RNA-seq on cellular RNA (cRNA) and cell-derived evRNA was done to determine if EVs could be used to predict the CMS subtype of their cells of origin. We then sought to perform molecular subtyping of plasma EVs from patients with CRC. RNA-seq was performed on tumor tissues and matched plasma EVs from 46 patients with CRC and 59 healthy controls (age/gender matched). The bulk transcriptome from CRC plasma EVs was deconvoluted to predict the cancer percentage (CIBERSORTx) and to utilize cancer-specific transcriptome for CMS subtyping (DeMixT). Artificial neural network (ANN)-based CMS subtype classifier was used to classify molecular subtypes. Results: There was 100% concordance between CMS subtype of evRNA with that of the cRNA. We showed that, RNA mixtures containing as low as 1% cancer cell RNA could be accurately classified into the correct CMS class. Imputed proportions of cancer in plasma of CRC patients ranged from 0.89% to 2.08%. Receiver operating characteristic (ROC) curve showed area under the curve of 0.961 with specificity and sensitivity of 0.96 and 0.9, respectively. Plasma evRNA was classified into CMS classes and there was 67% (31/46) concordance between the predicted subtype of liquid biopsies and the tumor samples. In patients with tumor purity greater than 10%, the concordance was higher at 75% (27/36). Conclusions: EVs could be used to accurately predict CMS subtypes of their cells of origin. We created a pipeline using low-input RNA library preparation from plasma EV to estimate the cancer RNA portion present in the bulk transcriptome and predict the molecular subtypes of colorectal cancers. Molecular subtyping of evRNA may help to track CMS changes of the tumor in patients undergoing treatment. Citation Format: Vahid Bahrambeigi, Jaewon J. Lee, Kimal I. Rajapakshe, Bret M. Stephens, Jason T. Henry, Sarah Dhebat, Mark W. Hurd, Ryan Sun, Scott Kopetz, Anirban Maitra, Paola A. Guerrero. Transcriptomic profiling of liquid biopsy in colorectal cancer [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer; 2022 Oct 1-4; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_1):Abstract nr PR013.

Histone lysine demethylases (KDMs) have been reported in various malignances, which affect transcriptional regulation of tumor suppressor or oncogenes. However, the relationship between KDMs and formation of tumor microenvironment (TME) in gastric cancer (GC) remain unclear and need to be comprehensively analyzed.In the present study, 24 KDMs were obtained and consensus molecular subtyping was performed using the "NMF" method to stratify TCGA-STAD into three clusters. The ssGSEA and CIBERSORT algorithms were employed to assess the relative infiltration levels of various cell types in the TME. The KDM_score was devised to predict patient survival outcomes and responses to both immunotherapy and chemotherapy.Three KDM genes-related molecular subtypes were Figured out in GC with distinctive clinicopathological and prognostic features. Based on the robust KDM genes-related risk_score and nomogram, established in our work, GC patients’ clinical outcome can be well predicted. Furthermore, low KDM genes-related risk_score exhibited the more effective response to immunotherapy and chemotherapy.This study characterized three KDM genes-related TME pattern with unique immune infiltration and prognosis by comprehensively analyses of transcriptomic profiling. Risk_score was also built to help clinicians decide personalized anticancer treatment for GC patients, including in prediction of immunotherapy and chemotherapy response for patients.

Comparative molecular subtypes of index and metachronous gastric adenocarcinomas: a study of 42 Korean patients.

Introduction and ObjectiveIdentifying patients that benefit from cisplatin-based adjuvant chemotherapy is a major issue in the management of muscle-invasive bladder cancer (MIBC). The purpose of this study is to correlate “luminal” and “basal” type protein expression with histological subtypes, to investigate the prognostic impact on survival after adjuvant chemotherapy and to define molecular consensus subtypes of “double negative” patients (i.e., without expression of CK5/6 or GATA3).Materials and MethodsWe performed immunohistochemical (IHC) analysis of CK5/6 and GATA3 for surrogate molecular subtyping in 181 MIBC samples. The mRNA expression profiles for molecular consensus classification were determined in CK5/6 and GATA3 (double) negative cases using a transcriptome panel with 19.398 mRNA targets (HTG Molecular Diagnostics). Data of 110 patients undergoing radical cystectomy were available for survival analysis.ResultsThe expression of CK5/6 correlated with squamous histological subtype (96%) and expression of GATA3 was associated with micropapillary histology (100%). In the multivariate Cox-regression model, patients receiving adjuvant chemotherapy had a significant survival benefit (hazard ratio [HR]: 0.19 95% confidence interval [CI]: 0.1–0.4, p < 0.001) and double-negative cases had decreased OS (HR: 4.07; 95% CI: 1.5–10.9, p = 0.005). Double negative cases were classified as NE-like (30%), stroma-rich (30%), and Ba/Sq (40%) consensus molecular subtypes and displaying different histological subtypes.ConclusionImmunohistochemical-based classification was associated with histological subtypes of urothelial MIBC. IHC markers like CK5/6 and GATA3 that are used in pathological routine could help to identify patients with basal and luminal tumor characteristics. However, a two-sided classification system might not sufficiently reflect the heterogeneity of bladder cancer to make treatment decisions. Especially the group of IHC-double negative cases, as further analyzed by mRNA expression profiling, are a heterogeneous group with different implications for therapy.

Simple SummaryGastric adenocarcinoma (GAC) is most commonly classified based on a system developed by the Cancer Genome Atlas in 2014. However, this subtyping system cannot efficiently identify suitable candidates for immunotherapy. Because GAC is highly heterogeneous and closely related to CRC at the molecular and functional levels, we explored the clinical utility of CMS classification originally developed for CRC and found that the CMS subtyping system can efficiently classify GAC. CMS1-4 classifications in GAC recapitulated their corresponding CRC subtype characteristics. Notably, CMS1 predicted a favorable response to anti-PD-1 therapy, and CMS4 outperformed the classical TCGA subtyping prognostic prediction and identified patients with an unfavorable anti-PD-1 response. Strikingly, partitioning the CMS4 subtype by EMT activation identified an additional anti-PD-1-susceptible patient subgroup. These results provide new insights that may help to improve clinical outcomes in immunotherapy candidates.Background: Gastric adenocarcinoma (GAC) is highly heterogeneous and closely related to colorectal cancer (CRC) both molecularly and functionally. GAC is currently subtyped using a system developed by TCGA. However, with the emergence of immunotherapies, this system has failed to identify suitable treatment candidates. Methods: Consensus molecular subtypes (CMSs) developed for CRC were used for molecular subtyping in GAC based on public expression cohorts, including TCGA, ACRG, and a cohort of GAC patients treated with the programmed cell death 1 (PD-1) inhibitor pembrolizumab. All aspects of each subtype, including clinical outcome, molecular characteristics, oncogenic pathway activity, and the response to immunotherapy, were fully explored. Results: CMS classification was efficiently applied to GAC. CMS4, characterized by EMT activation, stromal invasion, angiogenesis, and the worst clinical outcomes (median OS 24.2 months), was the predominant subtype (38.8%~44.3%) and an independent prognostic indicator that outperformed classical TCGA subtyping. CMS1 (20.9%~21.5%) displayed hypermutation, low SCNV, immune activation, and best clinical outcomes (median OS > 120 months). CMS3 (17.95%~25.7%) was characterized by overactive metabolism, KRAS mutation, and intermediate outcomes (median OS 85.6 months). CMS2 (14.6%~16.3%) was enriched for WNT and MYC activation, differentiated epithelial characteristics, APC mutation, lack of ARID1A, and intermediate outcomes (median OS 48.7 months). Notably, CMS1 was strongly correlated with immunotherapy biomarkers and favorable for the anti-PD-1 drug pembrolizumab, whereas CMS4 was poorly responsive but became more sensitive after EMT-based stratification. Conclusions: Our study reveals the practical utility of CMS classification for GAC to improve clinical outcomes and identify candidates who will respond to immunotherapy.

P6-09-10: Results of multigene assay (MammaPrint®) and molecular subtyping (BluePrint®) substantially impact treatment decision making in early breast cancer: Final analysis of the WSG PRIME decision impact study

Simple SummaryAround 20 years ago, genomic profiling of breast carcinomas identified tumor subtypes with clinical implications and opened the door for a better understanding of breast cancer biology. The commercialization of multigene tests had a significant impact on clinical practice, and yet, controversy exists as to which methodology is best to inform the choice of therapy and existing recommendations are inconsistent and often driven by cost-effectiveness. Here we report data from a cohort of breast cancer patients in which pathological and molecular subtyping are directly compared in a clinical setting. The findings show that some patients with genomic low-risk tumors could receive unnecessary systemic therapy if only following the classical clinical parameters, while others could remain under-treated. This study suggests that to design precise treatment regimens for patients with early breast cancer, the conventional clinicopathological classification should be complemented with the robust prognostic information provided by molecular subtyping.Precise prognosis is crucial for selection of adjuvant therapy in breast cancer. Molecular subtyping is increasingly used to complement immunohistochemical and pathological classification and to predict recurrence. This study compares both outcomes in a clinical setting. Molecular subtyping (MammaPrint®, TargetPrint®, and BluePrint®) and pathological classification data were compared in a cohort of 143 breast cancer patients. High risk clinical factors were defined by a value of the proliferation factor Ki67 equal or higher than 14% and/or high histological grade. The results from molecular classification were considered as reference. Core needle biopsies were found to be comparable to surgery samples for molecular classification. Discrepancies were found between molecular and pathological subtyping of the samples, including misclassification of HER2-positive tumors and the identification of a significant percentage of genomic high risk T1N0 tumors. In addition, 20% of clinical low-risk tumors showed genomic high risk, while clinical high-risk samples included 42% of cases with genomic low risk. According to pathological subtyping, a considerable number of breast cancer patients would not receive the appropriate systemic therapy. Our findings support the need to determine the molecular subtype of invasive breast tumors to improve breast cancer management.

IntroductionGastric cancer is the fifth most common cancer worldwide and the fourth most common cause of cancer-related death. Two molecular subtyping classifications were recently introduced: The Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ACRG) classifications.MethodsWe classified a cohort of 283 gastric cancer patients undergoing surgery at Helsinki University Hospital between 2000 and 2009. We constructed a tumour tissue microarray immunostained for the following markers: microsatellite instability (MSI) markers MSH2, MSH6, MLH1, and PMS2; p53; E-cadherin; and EBERISH.ResultsIn the univariate survival analysis for disease-specific survival, the Epstein–Barr virus (EBV) -positive subtype exhibited the worst prognosis with a hazard ratio (HR) of 2.49 (95% confidence interval [CI] 1.19–5.25, p = 0.016) compared with the most benign subtype, chromosomal instability (CIN). Using TCGA’s classification, the genetically stable (GS) and MSI subtypes exhibited a worse survival compared with CIN (HR 1.73 [95% CI 1.15–2.60], p = 0.009 and HR 1.74 [95% CI 1.06–2.84], p = 0.027, respectively). Using the ACRG classification, the p53 aberrant subtype exhibited the best prognosis, whereas wild-type p53, MSI, and the epithelial–mesenchymal transition (EMT) subtypes exhibited poorer prognoses (EMT: HR 1.90 [95% CI 1.30–2.77], p < 0.001) when compared with aberrant p53.ConclusionsImmunohistochemical analysis can identify prognostically different molecular subtypes of gastric cancer. The method is inexpensive and fast, yet reveals significant information for clinical decision-making. However, our study did not find that either molecular subtyping performed better than the other classification. Thus, further development of the most optimal grouping of different molecular subtypes is still needed.

BackgroundMicrosatellite instability (MSI) and Epstein-Barr virus (EBV)-positive molecular subtypes exhibit complex immune responses in gastric cancer (GC), and PD-L1 has emerged as a prognostic biomarker associated with the cancer immune microenvironment. This study aimed to determine the prognostic value of molecular subtypes and whether the addition of PD-L1 would accurately predict the prognosis and guide postoperative chemotherapy for GC patients.MethodsWe performed molecular subtyping of tissue microarray slides from 226 GC patients who were treated with radical gastrectomy. The MSI status and PD-L1 expression were evaluated through immunohistochemistry (IHC) and EBV status through situ hybridization. Multiplex polymerase chain reaction (PCR) was also performed on 50 cases to validate the accuracy of IHC in defining MSI status. Differences in overall survival (OS) were assessed using the Kaplan-Meier method, log-rank test and Cox proportional hazards regression model.ResultsAmong the 226 GC patients, 52 (23.2%) patients were classified as the MSI subtype, 11 (4.9%) were EBV+ subtype, and 161 (71.9%) were MSS (Microsatellite stable) /EBV subtype according to TCGA analysis. Two patients were both positive for MSI and EBV infection. EBV+ cases showed higher PD-L1 positivity than MSI cases and MSS/EBV cases (81.8% vs. 50.0% vs. 35.4%, P = 0.003). Compared with the non-MSS/EBV (MSI or EBV+ cases) subgroup, GC patients with MSS/EBV were associated with the worst outcomes (HR = 1.610, 95% CI [1.0462.479], P = 0.031). MSS/EBV GCs alone could benefit from postoperative chemotherapy (HR = 0.452, 95% CI [0.2990.682], P<0.001), and PD-L1-positive expression could also predict a better prognosis (HR = 0.612, 95% CI [0.3890.962], P = 0.033) in this subgroup. Considering both chemotherapy efficacy and PD-L1 expression in the MSS/EBV subgroup, chemotherapy could improve the prognosis for PD-L1-negative MSS/EBV GCs (HR = 0.357, 95% CI [0.2170.587], P <0.001) but not PD-L1-positive MSS/EBV GCs.ConclusionsMolecular subtyping combined with PD-L1 expression could serve as a potential strategy to better predict prognosis and guide postoperative chemotherapy of GC patients.

SummaryColorectal cancer (CRC) is a molecularly heterogeneous disease arising from gradual accumulation of genetic and epigenetic changes. In the last decade, great efforts have been made to classify CRC according to molecular features. This has led to several proposals of molecular subtyping. Recently, consensus molecular subtypes (CMS) have been proposed based on the integration of previously existing categorizations and additional comprehensive molecular studies. Microsatellite instability (MSI) is a highly specific molecular feature in CRC with a therapeutic impact, for example for immunotherapy. MSI is recognized as a separate CMS subtype. Beyond MSI, molecular subtyping may also be helpful for further differentiating CRC into prognostically distinct groups and for identifying new treatment targets, particularly for CMS with more aggressive behavior and resistance to conventional systemic treatment. Molecular subtypes may also exhibit distinctive morphological features, which may open the horizon for morphomolecular diagnostics based on digital pathology and machine learning. This review article summarizes current aspects of the molecular pathology of CRC with a focus on molecular subtyping in the context of pathological features and therapeutic applications.

484 Background: Upper tract urothelial carcinoma (UTUC) comprises 5-10% of urothelial malignancies but demonstrates unique clinical and molecular characteristics compared to urothelial carcinoma of the bladder. Prior investigations have used bulk profiling of tumor tissue to identify molecular subtypes, classifying the majority of UTUC as luminal and T-cell depleted. However, bulk sequencing does not allow for analysis of the significant heterogeneity known to be present in urothelial tumors. Single-cell RNA sequencing (scRNA-seq) allows examination of intra-tumoral heterogeneity, clonality, and the complex interactions of the immune tumor microenvironment (TME). We sought to apply this technology to better characterize UTUC and the TME. Methods: Single cell RNA sequencing (scRNA-seq) was performed on nine UTUC tissue specimens from six different patients collected fresh via ureteroscopic biopsy using an established institutional process and the 10X Genomics platform. Sequencing reads were normalized and analyzed using R/Seurat package. We assessed the composition of each tumor specimen with known marker genes for molecular subtypes (luminal, basal, squamous, EMT, and claudin-low). We then assessed the composition of immune cells in each specimen using known marker genes. We compared high- and low-grade specimens by subtype composition and immune cell infiltrates. Results: Lineage density analyses demonstrate the intra- and inter-tumoral heterogeneity of the nine endoscopic samples analyzed by molecular subtype composition. There is higher expression of luminal and claudin-low subtypes across all samples. The high-grade samples have higher expression of squamous markers. There is significant heterogeneity of immune cell infiltrates in seven specimens (two specimens were excluded due to low CD45+ cell counts). There is higher macrophage infiltration in high-grade samples, which was the only significant difference (Wilcoxon two-sided p-value = 0.05). Conclusions: This is the first known study using scRNA-seq expression analysis to characterize the notable heterogeneity of high and low-grade UTUC and the associated TME. Lineage density analysis demonstrates high luminal gene expression across samples, which has been demonstrated on prior bulk sequencing studies. The immune TME is also heterogeneous, with notable increased infiltration of macrophages in high-grade disease. There are unique limitations to performing and analyzing scRNA-seq of fresh UTUC tissue specimens, thus data should be interpreted cautiously. However, this study demonstrates the marked heterogeneity of UTUC tumors and frames our current approaches to bulk molecular subtyping of urothelial cancers and immune deconvolution. Further high-resolution studies are needed to characterize UTUC and inform bulk-sequencing efforts.