canSAR: update to the cancer translational research and drug discovery knowledgebase

canSAR (https://cansar.ai) is the largest public cancer drug discovery and translational research knowledgebase. Now hosted in its new home at MD Anderson Cancer Center, canSAR integrates billions of experimental measurements from across molecular profiling, pharmacology, chemistry, structural and systems biology. Moreover, canSAR applies a unique suite of machine learning algorithms designed to inform drug discovery. Here, we describe the latest updates to the knowledgebase, including a focus on significant novel data. These include canSAR's ligandability assessment of AlphaFold; mapping of fragment-based screening data; and new chemical bioactivity data for novel targets. We also describe enhancements to the data and interface.

Quantitative Assessment and Prognostic Associations of the Immune Landscape in Ovarian Clear Cell Carcinoma

Ovarian clear cell carcinoma (OCCC) is a rare subtype of epithelial ovarian cancer characterised by a high frequency of loss-of-function ARID1A mutations and a poor response to chemotherapy. Despite their generally low mutational burden, an intratumoural T cell response has been reported in a subset of OCCC, with ARID1A purported to be a biomarker for the response to the immune checkpoint blockade independent of micro-satellite instability (MSI). However, assessment of the different immune cell types and spatial distribution specifically within OCCC patients has not been described to date. Here, we characterised the immune landscape of OCCC by profiling a cohort of 33 microsatellite stable OCCCs at the genomic, gene expression and histological level using targeted sequencing, gene expression profiling using the NanoString targeted immune panel, and multiplex immunofluorescence to assess the spatial distribution and abundance of immune cell populations at the protein level. Analysis of these tumours and subsequent independent validation identified an immune-related gene expression signature associated with risk of recurrence of OCCC. Whilst histological quantification of tumour-infiltrating lymphocytes (TIL, Salgado scoring) showed no association with the risk of recurrence or ARID1A mutational status, the characterisation of TILs via multiplexed immunofluorescence identified spatial differences in immunosuppressive cell populations in OCCC. Tumour-associated macrophages (TAM) and regulatory T cells were excluded from the vicinity of tumour cells in low-risk patients, suggesting that high-risk patients have a more immunosuppressive microenvironment. We also found that TAMs and cytotoxic T cells were also excluded from the vicinity of tumour cells in ARID1A-mutated OCCCs compared to ARID1A wild-type tumours, suggesting that the exclusion of these immune effectors could determine the host response of ARID1A-mutant OCCCs to therapy. Overall, our study has provided new insights into the immune landscape and prognostic associations in OCCC and suggest that tailored immunotherapeutic approaches may be warranted for different subgroups of OCCC patients.

CD117 (c-Kit) Is Expressed During CD8+ T Cell Priming and Stratifies Sensitivity to Apoptosis According to Strength of TCR Engagement

CD117 (cKit) is the receptor for stem cell factor (SCF) and plays an important role in early haemopoiesis. We show that CD117 is also expressed following priming of mature human CD8⁺ T cells in vitro and is detectable following primary infection in vivo. CD117 expression is mediated through an intrinsic pathway and is suppressed by IL-12. Importantly, the extent of CD117 expression is inversely related to the strength of the activating stimulus and subsequent engagement with cell-bound SCF markedly increases susceptibility to apoptosis. CD117 is therefore likely to shape the pattern of CD8⁺ T cell immunodominance during a primary immune response by rendering cells with low avidity for antigen more prone to apoptosis. Furthermore, CD117⁺ T cells are highly sensitive to apoptosis mediated by galectin-1, a molecule commonly expressed within the tumor microenvironment, and CD117 expression may therefore represent a novel and potentially targetable mechanism of tumor immune evasion.

Transcriptome analysis reveals distinct gene expression profiles in astrocytoma grades II-IV

BACKGROUND

Astrocytoma is the most prevalent form of primary brain cancer categorized into four histological grades by the World Health Organization. Investigation into individual grades of astrocytoma by previous studies has provided some insight into dysregulation of regulatory networks associated with increasing astrocytoma grades. However, further understanding of key mechanisms that distinguish different astrocytoma grades is required to facilitate targeted therapies.

METHODS

In this study, we utilized a large cohort of publicly available RNA sequencing data from patients with diffuse astrocytoma (grade II), anaplastic astrocytoma (grade III), primary glioblastoma (grade IV), secondary glioblastoma (grade IV), recurrent glioblastoma (grade IV), and normal brain samples to identify genetic similarities and differences between these grades using bioinformatics applications.

RESULTS

Our analysis revealed a distinct gene expression pattern between grade II astrocytoma and grade IV glioblastoma (GBM). We also identified genes that were exclusively expressed in each of the astrocytoma grades. Furthermore, we identified known and novel genes involved in key pathways in our study. Gene set enrichment analysis revealed a distinct expression pattern of transcriptional regulators in primary GBM. Further investigation into molecular processes showed that the genes involved in cell proliferation and invasion were shared across all subtypes of astrocytoma. Also, the number of genes involved in metastasis, regulation of cell proliferation, and apoptosis increased with tumor grade.

CONCLUSIONS

We confirmed existing findings and shed light on some important genes and molecular processes that will improve our understanding of glioma biology.