Identifying oncogenic drivers associated with increased risk of late distant recurrence in postmenopausal, estrogen receptor-positive, HER2-negative early breast cancer: results from the BIG 1-98 study

Concomitant PIK3CA and TP53 Mutations in Breast Cancer: An Analysis of Clinicopathologic and Mutational Features, Neoadjuvant Therapeutic Response, and Prognosis

PURPOSE

PIK3CA and TP53 are the most prevalently mutated genes in breast cancer (BC). Previous studies have indicated an association between concomitant PIK3CA/TP53 mutations and shorter disease-free survival. As its clinical utility remains largely unknown, we aimed to analyze the prognostic and predictive roles of this co-mutation.

METHODS

We retrospectively analyzed patients who were diagnosed with BC at Guangdong Provincial People's Hospital (GDPH) who underwent next-generation sequencing. The correlation of concomitant PIK3CA/TP53 mutations with clinicopathological and mutational characteristics, and neoadjuvant systemic therapy (NST) responses was analyzed. The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset was used to verify associations between concurrent mutations and survival outcomes.

RESULTS

In the GDPH cohort, concomitant PIK3CA/TP53 mutations were associated with more aggressive phenotypes, including human epidermal growth factor receptor 2 positive status, hormone receptor negative status, high Ki-67 expression, high histological grade, advanced TNM stage, and additional genetic alterations. Co-mutations also portended a worse response to NST, especially taxane-containing regimens, when compared with the TP53 mutant alone (odds ratio, 3.767; 95% confidence interval, 1.205-13.087; p = 0.028). A significant association was observed between concomitant PIK3CA/TP53 mutations and poor survival outcomes in the METABRIC cohort.

CONCLUSION

Concomitant PIK3CA/TP53 mutations not only suggested unfavorable features and poor prognosis in BC but also conferred less benefit to NST than TP53 mutations alone.

Empirical Study of Large-Scale HLA Simulation of Parallel Region-Matching Knowledge Recognition Algorithm Based on Region Matching

Most of the existing region-matching algorithms need to match all regions, resulting in a waste of computing resources, increasing the cost of simulation technology and data redundancy, and resulting in the reduction of network data stream transmission efficiency. This paper presents a parallel region-matching knowledge recognition algorithm. Combined with the shortcomings of existing matching algorithms, a simulation technology is constructed to realize the parallel matching of multiple regions in HLA distributed simulation. The algorithm can realize the parallel matching calculation of multiple changed regions in one simulation. At the same time, the basic idea based on mobile intersection is adopted in the matching calculation, and the historical information before and after the region range is moved is used. The matching is limited to the moving interval, and the moving crossover theory is applied to the matching calculation to realize the relevant historical information before and after the region. Simulation results show that the parallel region-matching knowledge recognition algorithm can support HLA distributed simulation evaluation. In the matching calculation, the basic idea based on moving intersection is adopted, and the matching is limited to the moving interval by using the historical information before and after the region is moved, which reduces a large number of irrelevant calculations. Theoretical analysis and experimental results show that the algorithm is particularly suitable for the application needs of building large-scale distributed simulation based on multi-core computing platform.

Circulating tumor DNA is readily detectable among Ghanaian breast cancer patients supporting non-invasive cancer genomic studies in Africa

Circulating tumor DNA (ctDNA) sequencing studies could provide novel insights into the molecular pathology of cancer in sub-Saharan Africa. In 15 patient plasma samples collected at the time of diagnosis as part of the Ghana Breast Health Study and unselected for tumor grade and subtype, ctDNA was detected in a majority of patients based on whole- genome sequencing at high (30×) and low (0.1×) depths. Breast cancer driver copy number alterations were observed in the majority of patients.