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Objective: Deep learning (DL) has shown promising results for improving mammographic breast cancer diagnosis. However, the impact of artificial intelligence (AI) on the breast cancer screening process has not yet been fully elucidated in terms of potential workload reduction. We aim to assess if AI-based triaging of breast cancer screening mammograms could reduce the radiologist's workload with non-inferior sensitivity. Methods: PubMed, EMBASE, Cochrane Central, and Web of Science databases were systematically searched for studies that evaluated AI algorithms on computer-aided triage of breast cancer screening mammograms. We extracted data from homogenous studies and performed a proportion meta-analysis with a random-effects model to examine the radiologist's workload reduction (proportion of low-risk mammograms that could be theoretically ruled out from human's assessment) and the software's sensitivity to breast cancer detection. Results: Thirteen studies were selected for full review, and three studies that used the same commercially available DL algorithm were included in the meta-analysis. In the 156,852 examinations included, the threshold of 7 was identified as optimal. With these parameters, radiologist workload decreased by 68.3% (95%CI 0.655–0.711, I ² = 98.76%, p < 0.001), while achieving a sensitivity of 93.1% (95%CI 0.882–0.979, I ² = 83.86%, p = 0.002) and a specificity of 68.7% (95% CI 0.684–0.723, I ² = 97.5%, p < 0.01). Conclusions: The deployment of DL computer-aided triage of breast cancer screening mammograms reduces the radiology workload while maintaining high sensitivity. Although the implementation of AI remains complex and heterogeneous, it is a promising tool to optimize healthcare resources. [ABSTRACT FROM AUTHOR]

PURPOSE: Poly ADP-ribose polymerase inhibitors (PARPi) are approved for patients with human epidermal growth factor receptor 2–negative metastatic breast cancer (mBC) and germline pathogenic/likely pathogenic variant (hereafter mutation) in the BRCA1 / 2 genes (g BRCA); however, clinical benefit has also been demonstrated in mBC with somatic BRCA1 / 2 mutations (s BRCA) or germline PALB2 mutations (g PALB2). This study aims to describe the genomic landscape of homologous recombination repair (HRR) gene alterations in mBC and assess PARPi treatment outcomes for patients with g BRCA compared with other HRR genes and by status of a novel homologous recombination deficiency signature (HRDsig). METHODS: A real-world (RW) clinico-genomic database (CGDB) of comprehensive genomic profiling (CGP) linked to deidentified, electronic health record–derived clinical data was used. CGP was analyzed for HRR genes and HRDsig. The CGDB enabled cohort characterization and outcomes analyses of 177 patients exposed to PARPi. RW progression-free survival (rwPFS) and RW overall survival (rwOS) were compared. RESULTS: Of 28,920 patients with mBC, g BRCA was detected in 3.4%, whereas the population with any BRCA alteration or g PALB2 increased to 9.5%. HRDsig+ represented 21% of patients with mBC. BRCA and g PALB2 had higher levels of biallelic loss and HRDsig+ than other HRR alterations. Outcomes on PARPi were assessed for 177 patients, and g BRCA and s BRCA / gPALB2 cohorts were similar: g BRCA versus s BRCA /g PALB2 rwPFS was 6.3 versus 5.4 months (hazard ratio [HR], 1.37 [0.77-2.43]); rwOS was 16.2 versus 21.2 months (HR, 1.45 [0.74-2.86]). Additionally, patients with HRDsig+ versus HRDsig– had longer rwPFS (6.3 v 2.8 months; HR, 0.62 [0.42-0.92]) and numerically longer rwOS (17.8 v 13.0 months; HR, 0.72 [0.46-1.14]). CONCLUSION: Patients with s BRCA and g PALB2 derive similar benefit from PARPi as those with g BRCA alterations. In combination, HRDsig+, s BRCA , and g PALB2 represent an additional 19% of mBC that can potentially benefit from PARPi. Randomized trials exploring a more inclusive biomarker such as HRDsig are warranted. This is real-world evidence supporting benefit of PARPi in sBRCA, gPALB2, and HRDsig. [ABSTRACT FROM AUTHOR]

Endocrine therapy is the main treatment for hormone receptor-positive (HR+) breast cancer. However, advanced tumors develop resistance to endocrine therapy, rendering it ineffective as the disease progresses. There are several molecular mechanisms of primary and secondary endocrine resistance. Resistance can develop due to either alteration of the estrogen receptor pathway (e.g., ESR1 mutations) or upstream growth factors signaling pathways (e.g., PI3K/Akt/mTOR pathway). Despite progress in the development of molecularly targeted anticancer therapies, the emergence of resistance remains a major limitation and an area of unmet need. In this article, we review the mechanisms of acquired endocrine resistance in HR+ advanced breast cancer and discuss current and future investigational therapeutic approaches. [ABSTRACT FROM AUTHOR]

microRNA-22 (miR-22) is an oncogenic miRNA whose up-regulation promotes epithelial-mesenchymal transition (EMT), tumor invasion, and metastasis in hormone-responsive breast cancer. Here we show that miR-22 plays a key role in triple negative breast cancer (TNBC) by promoting EMT and aggressiveness in 2D and 3D cell models and a mouse xenograft model of human TNBC, respectively. Furthermore, we report that miR-22 inhibition using an LNA-modified antimiR-22 compound is effective in reducing EMT both in vitro and in vivo. Importantly, pharmacologic inhibition of miR-22 suppressed metastatic spread and markedly prolonged survival in mouse xenograft models of metastatic TNBC highlighting the potential of miR-22 silencing as a new therapeutic strategy for the treatment of TNBC. [ABSTRACT FROM AUTHOR]

Simple Summary: Poly-adenosine diphosphate ribose polymerase (PARP) inhibitors (PARPi) are effective against tumors with mutations in DNA repair genes, most commonly in the BRCA1 and BRCA2 genes. Because these tumors are unable to repair their DNA, PARPi have been used to target DNA repair pathways and are useful in the treatment of breast cancers with some of these alterations. There are two FDA-approved PARPi for patients with breast cancer—olaparib and talazoparib. The data on olaparib and talazoparib in the treatment of breast cancer are summarized in this review, and we also explore potential future applications of PARPi beyond inherited BRCA mutations. Poly-adenosine diphosphate ribose polymerase (PARP) inhibitors (PARPi) are approved for BRCA1/2 carriers with HER2-negative breast cancer in the adjuvant setting with a high risk of recurrence as well as the metastatic setting. However, the indications for PARPi are broader for patients with other cancer types (e.g., prostate and ovarian cancer), involving additional biomarkers (e.g., ATM, PALB2, and CHEK) and genomic instability scores. Herein, we summarize the data on PARPi and breast cancer and discuss their use beyond BRCA carriers. [ABSTRACT FROM AUTHOR]

Approximately 5% to 10% of women diagnosed with breast cancer will have a pathogenic variant (PV) in a hereditary cancer susceptibility gene, and this has significant implications for the management of these patients and their relatives. Despite the benefits of genetic testing, many eligible patients with breast cancer never undergo testing because of various barriers, including complicated testing criteria such as those from the National Comprehensive Cancer Network (NCCN). In 2019, the American Society of Breast Surgeons (ASBrS) proposed germline genetic testing for all patients with breast cancer to increase the identification of PV carriers. In 2020, a Mayo Clinic study highlighted the limitations of these 2 genetic testing guidelines (NCCN and ASBrS) and proposed a hybrid approach of testing all women diagnosed with breast cancer by the age of 65 years and using NCCN criteria for older patients. This commentary presents an updated analysis of the Mayo Clinic data and discusses the rationale for using the age of 60 years rather than 65 years as the cutoff for this hybrid approach. Using an age at diagnosis of ≤60 or ≤65 years for the universal testing of patients with breast cancer detected more PVs (11.9% [16 of 134] and 15.7% [21 of 134], respectively) in comparison with using the NCCN criteria. Lowering the age for universal testing from 65 to 60 years maintained the sensitivity of detecting a PV at >90% while sparing testing for an additional 10% of women. Compared with the testing of all patients, the hybrid approach would allow 31% of all women with breast cancer to forgo testing and result in fewer variants of uncertain significance identified and, therefore, would decrease the chance of harm from misinterpretation of these variants. The identification of pathogenic variants in high‐risk breast cancer genes has a significant impact on the management of patients with breast cancer and their relatives, yet many women do not receive genetic testing because of various barriers, with one of the major reasons being complex genetic testing criteria. Testing every single patient with breast cancer is not cost‐effective and has the potential for harm through the mismanagement of benign or ambiguous genetic testing results. This commentary argues that a hybrid approach of testing all patients below a certain age threshold while restricting testing on the basis of guidelines for an older population is a better approach. [ABSTRACT FROM AUTHOR]

Activating mutations of I PIK3CA i are found in 25%-40% of estrogen receptor-positive (ER+), HER2-negative (HER2-) breast cancers (BC), and in 8% of ER-negative (ER-) BC.[1] [5] Two recent studies support the benefit of PI3K inhibition in combination with endocrine therapy. Brain MRI 6 weeks later demonstrated a 14% reduction in the sum of longest distances of measurable brain metastases (24% reduction in bidimensional areas) and regressions of nonmeasurable brain metastases. A recent study showed that I PIK3CA i -mutant BC not only has a higher probability to metastasize to the brain than nonmutant BC (31% I v i 17%) but also has shorter median overall survival after the diagnosis of CNS metastasis (0.5 I v i 1.1 year).[9] The high frequency of brain metastases and their poor prognosis raise the question whether PI3K inhibitors can be beneficial for patients with active brain metastases. Breast Cancer Res; 13: R125, 2011 9 Fitzgerald DM, Muzikansky A, Pinto C, et al: Association between PIK3CA mutation status and development of brain metastases in HR+/HER2- metastatic breast cancer. [Extracted from the article]

Background: Artificial intelligence (AI) has the potential to personalize treatment strategies for patients with cancer. However, current methodological weaknesses could limit clinical impact. We identified common limitations and suggested potential solutions to facilitate translation of AI to breast cancer management.Methods: A systematic review was conducted in MEDLINE, Embase, SCOPUS, Google Scholar and PubMed Central in July 2021. Studies investigating the performance of AI to predict outcomes among patients undergoing treatment for breast cancer were included. Algorithm design and adherence to reporting standards were assessed following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement. Risk of bias was assessed by using the Prediction model Risk Of Bias Assessment Tool (PROBAST), and correspondence with authors to assess data and code availability.Results: Our search identified 1,124 studies, of which 64 were included: 58 had a retrospective study design, with 6 studies with a prospective design. Access to datasets and code was severely limited (unavailable in 77% and 88% of studies, respectively). On request, data and code were made available in 28% and 18% of cases, respectively. Ethnicity was often under-reported (not reported in 52 of 64, 81%), as was model calibration (63/64, 99%). The risk of bias was high in 72% (46/64) of the studies, especially because of analysis bias.Conclusion: Development of AI algorithms should involve external and prospective validation, with improved code and data availability to enhance reliability and translation of this promising approach. Protocol registration number: PROSPERO - CRD42022292495. [ABSTRACT FROM AUTHOR]