Analysis of the transcriptomic features of microsatellite instability subtype colon cancer

Unique miRNA Expression Profile in MSI- and EMAST-Unstable Sporadic Colon Cancer

MicroRNAs (miRNAs) are critical post-transcriptional gene regulators and their involvement in sporadic colon cancer (CRC) tumorigenesis has been confirmed. In this study we investigated differences in miRNA expression in microsatellite stable (MSS/EMAST-S), microsatellite unstable marked by high elevated microsatellite alterations at selected tetranucleotide repeats (MSS/EMAST-H), and high microsatellite unstable (MSI-H/EMAST-H) tumor subgroups as well as in tumors with different clinicopathologic characteristics. An RT-qPCR analysis of miRNA expression was carried out on 45 colon cancer and adjacent normal tissue samples (15 of each group). Overall, we found three differentially expressed miRNAs between the subgroups. miR-92a-3p and miR-224-5p were significantly downregulated in MSI-H/EMAST-H tumors in comparison to other subgroups. miR-518c-3p was significantly upregulated in MSS/EMAST-H tumors in comparison to stable and highly unstable tumors. Furthermore, we showed that miR-143-3p and miR-145-5p were downregulated in tumors in comparison to normal tissues in all subgroups. In addition, we showed overexpression of miR-125b-5p in well-differentiated tumors and miR-451a in less advanced tumors. This is the first report on differences in miRNA expression profiles between MSS/EMAST-S, MSS/EMAST-H, and MSI-H/EMAST-H colorectal cancers. Our findings indicate that the miRNA expression signatures differ in CRC subgroups based on their instability status.

T-cell priming transcriptomic markers: implications of immunome heterogeneity for precision immunotherapy

Immune checkpoint blockade is effective for only a subset of cancers. Targeting T-cell priming markers (TPMs) may enhance activity, but proper application of these agents in the clinic is challenging due to immune complexity and heterogeneity. We interrogated transcriptomics of 15 TPMs (CD137, CD27, CD28, CD80, CD86, CD40, CD40LG, GITR, ICOS, ICOSLG, OX40, OX40LG, GZMB, IFNG, and TBX21) in a pan-cancer cohort (N = 514 patients, 30 types of cancer). TPM expression was analyzed for correlation with histological type, microsatellite instability high (MSI-H), tumor mutational burden (TMB), and programmed death-ligand 1 (PD-L1) expression. Among 514 patients, the most common histological types were colorectal (27%), pancreatic (11%), and breast cancer (10%). No statistically significant association between histological type and TPM expression was seen. In contrast, expression of GZMB (granzyme B, a serine protease stored in activated T and NK cells that induces cancer cell apoptosis) and IFNG (activates cytotoxic T cells) were significantly higher in tumors with MSI-H, TMB ≥ 10 mutations/mb and PD-L1 ≥ 1%. PD-L1 ≥ 1% was also associated with significantly higher CD137, GITR, and ICOS expression. Patients' tumors were classified into "Hot", "Mixed", or "Cold" clusters based on TPM expression using hierarchical clustering. The cold cluster showed a significantly lower proportion of tumors with PD-L1 ≥ 1%. Overall, 502 patients (98%) had individually distinct patterns of TPM expression. Diverse expression patterns of TPMs independent of histological type but correlating with other immunotherapy biomarkers (PD-L1 ≥ 1%, MSI-H and TMB ≥ 10 mutations/mb) were observed. Individualized selection of patients based on TPM immunomic profiles may potentially help with immunotherapy optimization.

Challenges and Therapeutic Opportunities in the dMMR/MSI-H Colorectal Cancer Landscape

About 5 to 15% of all colorectal cancers harbor mismatch repair deficient/microsatellite instability-high status (dMMR/MSI-H) that associates with high tumor mutation burden and increased immunogenicity. As a result, and in contrast to other colorectal cancer phenotypes, a significant subset of dMMR/MSI-H cancer patients strongly benefit from immunotherapy. Yet, a large proportion of these tumors remain unresponsive to any immuno-modulating treatment. For this reason, current efforts are focused on the characterization of resistance mechanisms and the identification of predictive biomarkers to guide therapeutic decision-making. Here, we provide an overview on the new advances related to the diagnosis and definition of dMMR/MSI-H status and focus on the distinct clinical, functional, and molecular cues that associate with dMMR/MSI-H colorectal cancer. We review the development of novel predictive factors of response or resistance to immunotherapy and their potential application in the clinical setting. Finally, we discuss current and emerging strategies applied to the treatment of localized and metastatic dMMR/MSI-H colorectal tumors in the neoadjuvant and adjuvant setting.

Proteomic characterization of gastric cancer response to chemotherapy and targeted therapy reveals new therapeutic strategies

Chemotherapy and targeted therapy are the major treatments for gastric cancer (GC), but drug resistance limits its effectiveness. Here, we profile the proteome of 206 tumor tissues from patients with GC undergoing either chemotherapy or anti-HER2-based therapy. Proteome-based classification reveals four subtypes (G-I-G-IV) related to different clinical and molecular features. MSI-sig high GC patients benefit from docetaxel combination treatment, accompanied by anticancer immune response. Further study reveals patients with high T cell receptor signaling respond to anti-HER2-based therapy; while activation of extracellular matrix/PI3K-AKT pathway impair anti-tumor effect of trastuzumab. We observe CTSE functions as a cell intrinsic enhancer of chemosensitivity of docetaxel, whereas TKTL1 functions as an attenuator. Finally, we develop prognostic models with high accuracy to predict therapeutic response, further validated in an independent validation cohort. This study provides a rich resource for investigating the mechanisms and indicators of chemotherapy and targeted therapy in GC.

RNA Sequencing Data for FFPE Tumor Blocks Can Be Used for Robust Estimation of Tumor Mutation Burden in Individual Biosamples

Tumor mutation burden (TMB) is a well-known efficacy predictor for checkpoint inhibitor immunotherapies. Currently, TMB assessment relies on DNA sequencing data. Gene expression profiling by RNA sequencing (RNAseq) is another type of analysis that can inform clinical decision-making and including TMB estimation may strongly benefit this approach, especially for the formalin-fixed, paraffin-embedded (FFPE) tissue samples. Here, we for the first time compared TMB levels deduced from whole exome sequencing (WES) and RNAseq profiles of the same FFPE biosamples in single-sample mode. We took TCGA project data with mean sequencing depth 23 million gene-mapped reads (MGMRs) and found 0.46 (Pearson)–0.59 (Spearman) correlation with standard mutation calling pipelines. This was converted into low (<10) and high (>10) TMB per megabase classifier with area under the curve (AUC) 0.757, and application of machine learning increased AUC till 0.854. We then compared 73 experimental pairs of WES and RNAseq profiles with lower (mean 11 MGMRs) and higher (mean 68 MGMRs) RNA sequencing depths. For higher depth, we observed ~1 AUC for the high/low TMB classifier and 0.85 (Pearson)–0.95 (Spearman) correlation with standard mutation calling pipelines. For the lower depth, the AUC was below the high-quality threshold of 0.7. Thus, we conclude that using RNA sequencing of tumor materials from FFPE blocks with enough coverage can afford for high-quality discrimination of tumors with high and low TMB levels in a single-sample mode.

Macrophages, as a Promising Strategy to Targeted Treatment for Colorectal Cancer Metastasis in Tumor Immune Microenvironment

The tumor immune microenvironment plays a vital role in the metastasis of colorectal cancer. As one of the most important immune cells, macrophages act as phagocytes, patrol the surroundings of tissues, and remove invading pathogens and cell debris to maintain tissue homeostasis. Significantly, macrophages have a characteristic of high plasticity and can be classified into different subtypes according to the different functions, which can undergo reciprocal phenotypic switching induced by different types of molecules and signaling pathways. Macrophages regulate the development and metastatic potential of colorectal cancer by changing the tumor immune microenvironment. In tumor tissues, the tumor-associated macrophages usually play a tumor-promoting role in the tumor immune microenvironment, and they are also associated with poor prognosis. This paper reviews the mechanisms and stimulating factors of macrophages in the process of colorectal cancer metastasis and intends to indicate that targeting macrophages may be a promising strategy in colorectal cancer treatment.

Controlling for cellular heterogeneity using single-cell deconvolution of gene expression reveals novel markers of colorectal tumors exhibiting microsatellite instability

Approximately 15% of colorectal cancer (CRC) cases present with high levels of microsatellite instability (MSI-H). Bulk RNA-sequencing approaches have been employed to elucidate transcriptional differences between MSI-H and microsatellite stable (MSS) CRC tumors. These approaches are frequently confounded by the complex cellular heterogeneity of tumors. We performed single-cell deconvolution of bulk RNA-sequencing on The Cancer Genome Atlas colon adenocarcinoma (TCGA-COAD) dataset. Cell composition within each dataset was estimated using CIBERSORTx. Cell composition differences were analyzed using linear regression. Significant differences in abundance were observed for 13 of 19 cell types between MSI-H and MSS/MSI-L tumors in TCGA-COAD. This included a novel finding of increased enteroendocrine (q = 3.71E-06) and reduced colonocyte populations (q = 2.21E-03) in MSI-H versus MSS/MSI-L tumors. We were able to validate some of these differences in an independent biopsy dataset. By incorporating cell composition into our regression model, we identified 3,193 differentially expressed genes (q = 0.05), of which 556 were deemed novel. We subsequently validated many of these genes in an independent dataset of colon cancer cell lines. In summary, we show that some of the challenges associated with cellular heterogeneity can be overcome using single-cell deconvolution, and through our analysis we highlight several novel gene targets for further investigation.

Artificial Intelligence for Histology-Based Detection of Microsatellite Instability and Prediction of Response to Immunotherapy in Colorectal Cancer

Microsatellite instability (MSI) is a molecular marker of deficient DNA mismatch repair (dMMR) that is found in approximately 15% of colorectal cancer (CRC) patients. Testing all CRC patients for MSI/dMMR is recommended as screening for Lynch Syndrome and, more recently, to determine eligibility for immune checkpoint inhibitors in advanced disease. However, universal testing for MSI/dMMR has not been uniformly implemented because of cost and resource limitations. Artificial intelligence has been used to predict MSI/dMMR directly from hematoxylin and eosin (H&E) stained tissue slides. We review the emerging data regarding the utility of machine learning for MSI classification, focusing on CRC. We also provide the clinician with an introduction to image analysis with machine learning and convolutional neural networks. Machine learning can predict MSI/dMMR with high accuracy in high quality, curated datasets. Accuracy can be significantly decreased when applied to cohorts with different ethnic and/or clinical characteristics, or different tissue preparation protocols. Research is ongoing to determine the optimal machine learning methods for predicting MSI, which will need to be compared to current clinical practices, including next-generation sequencing. Predicting response to immunotherapy remains an unmet need.

Durvalumab activity in previously treated patients who stopped durvalumab without disease progression

BACKGROUND

Limited data exist on potential clinical benefit with anti-programmed cell death ligand-1 (PD-L1) retreatment in patients who stop initial therapy for reasons other than disease progression or toxicity and develop disease progression while off treatment.

PATIENTS AND METHODS

NCT01693562 was a phase I/II study evaluating durvalumab monotherapy in advanced solid tumors. Patients benefiting from treatment were taken off durvalumab at 1 year per protocol and prospectively followed. At disease progression, they were eligible for durvalumab retreatment. Outcomes evaluated during retreatment included best overall response (BOR2), duration of response (DoR2), disease control rate (DCR2), and progression-free survival (PFS2).

RESULTS

Of 980 patients enrolled and treated with durvalumab 10 mg/kg every 2 weeks (Q2W) in the dose-expansion cohorts, 168 completed 1 year of initial durvalumab treatment with confirmed BOR1 of complete response in 20 (11.9%), partial response (PR) in 84 (50%), stable disease (SD) in 52 (31%), and disease progression in 12 (7.1%). All 168 patients stopped treatment and were eligible for retreatment at progression; 70 patients (41.7%) representing 14 primary tumor types were retreated and response evaluable. Confirmed BOR2 was PR in 8 patients (11.4%), SD in 42 (60.0%), disease progression in 16 (22.9%), and unevaluable in 4 (5.7%). Median DoR2 was 16.5 months. DCR2 ≥24 weeks (DCR2 24) was 47.1%. PFS2 rate at 12 months was 34.2%, and median PFS2 was 5.9 months. Median overall survival (OS2) was 23.8 months. Response rates, DCR2 24, and median DoR2 were generally greater in patients with high PD-L1 expression than those with low/negative expression. No new safety signals were observed during retreatment.

CONCLUSION

Retreatment restored antitumor activity, resulting in high rates of durable disease control with an acceptable safety profile. This evidence supports retreatment of patients who stop anti-PD-L1 therapy for reasons other than progression or toxicity, and supports further investigation.