Oncology Insights

Squamous Cell Carcinomas of the Cornea: A Case Report

Dipin Jayaprakash — 2022-03-17

Squamous cell carcinomas of the cornea are rare tumours. After excision, adjuvant treatment in the form of topical Interferon is commonly used to prevent recurrences and also for margin positive excisions. Ours is a chronic hepatitis B patient who underwent excisional biopsy and presented as corneal perforation while on topical adjuvant Interferon therapy. It can be secondary to previous surgery or unusual presentation, such as tumour perforation. The secondary role of initiating IFN therapy causing perforation is a possibility, and the complication needs to be assessed further. It may be prudent to avoid interferon drops in patients with compromised ocular surface tectonic stability and immune-compromised individuals. Plaque brachytherapy would be a better adjuvant in these cases.

Integrative Analysis of Genetic and Epigenetic Alterations in the CBX7 Gene Reveals Its Tumor-Suppressive Function by Regulating the Cell Cycle in Human Breast Cancer

Yimeng Cai — 2023-03-05

CBX7 is a member of the chromobox gene family, which plays an important role in epigenetic transcriptional regulation. In this study, we found that compared to normal mammary tissues, mRNA levels of CBX7 are consistently significantly downregulated in breast cancers (BCs) across different datasets. Integrative multiomics analysis revealed the genetic and epigenetic mechanisms for the loss of CBX7 expression in BCs. Lower expression levels of CBX7 are significantly associated with shorter overall, disease-free, and distant metastasis-free survival of patients with BC. These prognostic impacts of CBX7 are independent of estrogen receptor status and PAM50 molecular subtypes. Coexpression analysis identified 207 genes consistently coexpressed with CBX7 (157 negatively and 50 positively). Gene Ontology, KEGG, and Reactome enrichment analysis revealed that cell cycle‑, DNA replication‑, and mitosis-related pathways are significantly overrepresented within the set of CBX7 negatively coexpressed genes, suggesting that CBX7 functions as a suppressor of the cell cycle. Moreover, transcription factor enrichment analysis detected the E2F family of transcription factors significantly associated with CBX7 negatively coexpressed genes, consistent with E2F function regulating the cell cycle. Furthermore, we found that loss of CBX7 expression significantly increases genomic instability and tumor mutation burden. Our findings indicate that CBX7 acts as a tumor suppressor in BC through its potential role in the negative regulation of cell proliferation and the maintenance of genome integrity.

The Prognostic Value of Classical Immunoparesis in Multiple Myeloma

Rafael Ríos-Tamayo — 2022-02-08

Multiple myeloma (MM) is a very heterogeneous hematological malignancy characterized by the proliferation of clonal plasma cells in bone marrow, leading to a decrease in normal plasma cells. The immune system plays a key role in both the pathogenesis and the prognosis of MM. A wide range of immune dysfunctions can be demonstrated in most patients at diagnosis. The presence of suppression of uninvolved immunoglobulins, also called classical immunoparesis (CIP), can be demonstrated in the majority of newly diagnosed MM (NDMM) patients, although its prognostic impact remains controversial in previous studies. Our population-based study confirms that CIP is present in most NDMM patients. It is associated with several well-known prognostic factors, including the International Staging System, being more frequent in late stages. Median overall survival in CIP+ patients was 62.4 months (CI 95%, 52.1-72.7), whereas it was not reached for those CIP- (p=0.150). Despite the absence of statistical significance, the multivariate Cox proportional hazards model endorses CIP as an independent and strong prognostic factor for overall survival in NDMM, besides age, performance status, total serum cholesterol, and the presence of 1q gain. More comprehensive studies, including complete immune profiling, are warranted to establish the role of CIP in the context of the current and emerging prognostic factors in NDMM.

Using Artificial Intelligence and Magnetic Resonance Imaging to Address Limitations in Response Assessment in Glioma

Andra Krauze — 2022-04-01

Gliomas are rapidly progressive, neurologically devastating, nearly uniformly fatal brain tumors. In WHO grade IV tumors like glioblastoma, the standard of care involves maximal surgical resection followed by concurrent radiation therapy (RT) and temozolomide (TMZ) chemotherapy followed by adjuvant TMZ. This results in overall survival (OS) of less than 30% at two years. Currently, tumor progression assessment is based on clinician assessment and MRI interpretation using Response Assessment in Neuro-Oncology (RANO) criteria. These criteria classify response as complete, partial, stable, or progression. This approach, however, suffers from significant limitations due to the difficulty in interpreting MRI findings on T1 gad and T2 FLAIR sequences, lack of concurrent correlation with radiation therapy fields, inconsistent follow-up imaging, concurrent administration of steroids, and systemic management, including immunotherapy. The neuro-oncology field struggles with classifying true progression vs. pseudoprogression vs. pseudoresponse with progression guidelines actively evolving. The lack of consensus on the definition of progression impairs the ability to initiate earlier management upon progression, judge the impact of therapies, and optimize and personalize management. Due to the pivotal role of imaging, radiology is at the center of the question of optimizing and advancing response criteria [1-5]. The hypothesis is that MRI images of patients with glioma, when subjected to change over time analysis (at diagnosis, prior to and post-radiation therapy), can identify features predictive of treatment failure helping guide patient management in the clinic. Likely a combination of imaging and biospecimen-driven biomarkers is needed. Given the large amount of data generated by both approaches, success in this space hinges on leveraging computational approaches and artificial intelligence algorithms validated using large-scale publicly available data sets to disentangle the complexity and heterogeneity inherent in glioma progression.