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Analysis of triple-negative breast cancer samples reveals new markers that may aid diagnosis and ultimately treatment

Breast cancer is the most common cancer in females in the UK, with around 56 800 new cases diagnosed every year.

Ten-year survival rates depend on the stage of diagnosis: 96.0% and 79.1% for diagnosis at stages 1 and 2, respectively; and dropping to 52.7% and 11.6% for diagnosis stages 3 and 4, respectively.

However, as most diagnosis now occurs at disease stages 1 and 2 (in the England in 2018, almost six times the number of cases were diagnosed at stages 1 and 2 compared to stages 3 and 4), on average, nearly 76% of women diagnosed with breast cancer survive their disease for 10 years or more. Additionally, survival rates have doubled in the last 50 years. Of course, many different factors feed in to these improved survival rates.

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Photomicrograph of a breast cancer (grade 3 invasive ductal carcinoma) with frequent mitoses (mitotic figures), including a large central atypical mitosis (Adobe Stock.com)

For example, screening services and promotion of routine self-examination has helped earlier detection of disease. Also, a better understanding of the molecular landscape of the disease has resulted in improved therapies by being able to target the markers that drive the disease in some subtypes: estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. However, triple-negative breast cancer (TNBC), which comprises approximately 15% of breast cancer cases, lacks these markers and has a worse prognosis with more frequent relapse and lower survival rates than other types of breast cancer. TNBC is also a more diverse disease, and a number of subtypes have been identified.

In a recent study, therefore, Rapier-Sharman N et al. performed a secondary transcriptomic analysis with the aim of further understanding the pathological mechanisms of TNBC in the hope of identifying mechanistic markers that can improve diagnosis and be targets for therapy. The authors analysed 196 samples selected from the Gene Expression Omnibus database that included TNBC samples as well as healthy breast tissue samples. The study identifies the 10 most statistically significant differentially expressed genes (DEGs).

The authors then identified the top five up- and down-regulated mechanistic markers that distinguish TNBC from healthy tissue, before going on to identify mechanistic markers that are capable of differentiating TNBC subtypes. The findings identified two new DEGs (FO082814.1 and ELMOD3) in addition to previously identified ones (including KIF14, ASPM, KIF11 and RGS1, among others). With regards to mechanistic markers, the study identified a new marker (TNMD) again in addition ones already known (including CIDEC, CD300LG, ASPM, RGS1, CFD, among others). Of the mechanistic markers, the authors identified four that have known drugs on the Open Targets database and so have potential for drug repurposing: IFNG, ADM, PDE3B and CFD). Although the study has limitations, the authors conclude that their “tri-fold verification gives us confidence that our results consist of real biological phenomena and supports the future study of CIDEC and TNMD as mechanistic markers, FO082814.1 as a novel TNBC-associated pseudogene, and KIF14 as a therapeutic target”. The more we can learn about this form of breast cancer, the faster we can develop effective treatment and so improve survival rates.

Abbreviations

ADM, adrenomedullin; ASPM, abnormal spindle microtubule assembly; CD300LG, CD300 molecule like family member G; CFD, complement factor D; CIDEC, cell death inducing DFFA like effector C; ELMOD3, ELMO domain containing 3; FO082814.1, a methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 1-like pseudogene; IFNG, interferon gamma; KIF11, kinesin family member 11; KIF14, kinesin family member 14); PDE3B, phosphodiesterase 3B; RGS1, regulator of G protein signaling 1; and TNMD, tenomodulin.

Read the research
Secondary transcriptomic analysis of triple-negative breast cancer reveals reliable universal and subtype-specific mechanistic markers. Rapier-Sharman N, Spendlove MD, Poulsen JB et al. Cancers (Basel) 2024;16(19):3379 (https://www.mdpi.com/2072-6694/16/19/3379).