Hologic (United States)

531 Background: The Breast Cancer Index (BCI) is a validated gene expression assay that provides an individualized risk of late distant recurrence and predicts the likelihood of benefit from extended endocrine therapy (EET) in HR+ early-stage breast cancer. The objective of this analysis was to assess the influence of BCI on clinical decision-making regarding EET. Methods: The BCI Registry study is a prospective, multi-institutional study investigating the long-term clinical outcome, decision impact, and quality of life in HR+ breast cancer patients receiving BCI testing as part of routine clinical care. Physicians and patients completed pre- and post-BCI test questionnaires to assess physician decision-making; physician confidence; and patient preferences and concerns for EET. Pre- and post-BCI responses were compared using McNemar’s test and the Wilcoxon signed rank test. The BCI Registry Study is registered on ClinicalTrials.gov under NCT04875351. Results: In the current analysis,pre- and post-BCI testing questionnaires were completed for 2850 physicians and 2832 patients. 88.6% of patients were postmenopausal, 76.5% N0, 73.0% T1, 53.5% G2, and 13.0% HER2-positive. Following BCI testing, physicians changed treatment recommendations for EET in 41.2% (1175/2850) of patients (p<0.001). In cases where physicians recommended EET prior to BCI testing, 49.8% (775/1555) changed their recommendation to not treat with EET, while 31.2% (400/1280) of those who did not recommend EET prior to BCI testing changed their recommendation in favor of EET. Following BCI testing, 43.9% (1250/2850) of physicians felt more confident in their recommendation (p<0.001) and 43.2% (1223/2832) of patients felt more comfortable with their EET decision (p<0.001). The percentage of physicians having high confidence levels (confident or strongly confident) increased from 63.6% (N=1813) pre-BCI testing to 88.2% (N=2515) post-BCI testing. The percentage of physicians having low confidence levels (not at all confident, not confident, or ambivalent) decreased from 33.1% (N=943) pre-BCI testing to 11.0%(N=313) post-BCI testing. In BCI (H/I)-Low patients, 48.9% (868/1776) showed a decreased preference for EET (p<0.001). In BCI (H/I)-High patients, 34.6% (365/1056) showed an increased EET preference (p<0.001). After BCI testing, significantly more patients were less concerned about cost (23.9%, p<0.001), drug safety (25.7%, p<0.001), and EET benefit (30.9%, p<0.001). No significant change in concern regarding side-effects was observed (p=0.58). Conclusions: Incorporating BCI into clinical practice resulted in significant changes in physician recommendations for EET, while at the same time increasing physician confidence. Knowledge of the BCI result improved patient preference, satisfaction and reduced patient concerns regarding cost, drug safety and benefit of EET.

456 Background: CUP is a rare and heterogenous clinicopathologic syndrome with unidentifiable origin at time of diagnosis. Based on immunophenotyping, specifically PAX8 staining, CUP with Renal profile (RCUP) is emerging as a distinct clinical subset. In addition to clinical presentation and immunohistochemistry (IHC), molecular profiling for tissue of origin may aid defining CUP subsets. Precision in diagnosis impacts therapeutics given that cytotoxic chemotherapy options for CUP do not overlap with tyrosine kinase inhibitors or checkpoint inhibitors used for renal cell carcinoma. Methods: We retrospectively (2018-2022) evaluated patients with CUP composed of cases (renal profile, N=100, clear cell or papillary) or controls (non-renal, N=200) identified using the 92-gene assay (CancerTYPE ID), a validated classifier for predicting tissue of origin. Clinicopathologic data including IHC and molecular profiling results were collected using pathology specimens and reports. Results: Baseline characteristics (age, gender, tumor grade) of cases and controls were comparable, except for histology, wherein adenocarcinoma was less common with RCUP (27% vs 48%, p<0.001). Median number of IHC stains performed was 11 (range 0-30) vs 9 (0-29) (p=0.04) in RCUP vs non-renal cases, respectively. While PAX8 was tested in 64% vs 39% (p<0.001) of cases, it was found to be strongly or focally positive in 87.5% vs 21.8% (p<0.001), respectively. In contrast to non-renal CUP, RCUP patients were more likely to have bone biopsies (24% vs 11.5%, OR 2.4, p=0.007), less likely to have high-TMB (2.8% vs 28.2%, OR 0.07, p=0.001), and had a lower proportion of TP53 mutations (18.8% vs 89.2%, OR 0.028, p<0.001), KRAS mutations (13.3% vs 77.4%, OR 0.045, p<0.001) and BRAF mutations (0% vs 42%, OR 0, p=0.015). Conclusions: RCUP appears to be a distinct subset of CUP with variable diagnostic workup. Critical IHC markers may be omitted in a considerable number of cases and despite positivity, may still confer an uncertain diagnosis. Key molecular factors appear to be different and may require tailored therapeutic approaches. This study further supports the clinical utility of GEP-based cancer classification with the 92-gene assay to predict RCUP and may be an important adjunct to IHC to improve diagnostic accuracy for CUP and to guide treatment selection for better clinical outcomes. RCUP (N=100) Non-renal CUP (N=200) IHC Cases Tested (%) Cases Positive (%) Cases Tested (%) Cases Positive (%) CK7 83 47 81 75* CK20 75 11 77 24* PAX8 64* 88* 39 22 Napsin 29 28* 25 8 RCC 26* 31 4 13 Vimentin 17 94 5.5 73 *Significantly (p<0.05) greater proportion of cases.

Abstract Biology has been transformed by the rapid development of computing and the concurrent rise of data-rich approaches such as -omics or high-resolution imaging. However, there is a persistent computational skills gap in the biomedical research workforce. Inherent limitations of classroom teaching and institutional core support highlight the need for accessible ways for researchers to explore developments in computational biology. An analysis of the Scripps Research Genomics Core revealed an increasingly diverse set of experiments: the share of experiments other than bulk RNA- or DNA-seq increased from 34% to 60% within 10 years, requiring more tailored computational analyses. These challenges were tackled by forming a volunteer-led affinity group of over 300 academic biomedical researchers interested in computational biology referred to as the Computational Biology and Bioinformatics (CBB) affinity group. This adaptive group has provided continuing education and networking opportunities through seminars, workshops and coding sessions while evolving along with the needs of its members. A survey of CBB’s impact confirmed the group’s events increased the members’ exposure to computational biology educational and research events (79% respondents) and networking opportunities (61% respondents). Thus, volunteer-led affinity groups may be a viable complement to traditional institutional resources for enhancing the application of computing in biomedical research.

Microbes play vital roles in ecological systems, yet their presence and functions within venom environments of venomous organisms remain understudied. Despite the prevalent belief in the sterility of venoms, recent findings reveal diverse microbial communities within venom systems. This review aims to explore the relationships between venoms and microbes, highlighting their potential roles in evolutionary processes, ecological interactions, and therapeutic advancements. Venoms, composed of toxins utilized in hunting or defense, represent a rich source of natural products with applications in drug discovery and therapy, exemplified by FDA-approved venom toxin-derived drugs. Understanding microbial resistance mechanisms against antimicrobial peptides can illuminate coevolutionary processes and guide therapeutic development. Integrating hologenomic evolution and microbial ecology frameworks will facilitate comprehensive research on venom-microbiome interactions, and reveal the evolutionary drivers of venom diversification. Investigating and investing in these relationships promises advancements in understanding evolution, ecology, and biotechnology, with implications for human health and ecological conservation. This review synthesizes existing knowledge, identifies many gaps in literature, and investigates critical unanswered questions in the field of venom microbiology, encouraging ongoing and future collaborative research.

Cancer of unknown primary (CUP) is a syndrome comprising metastatic cancers without a clinically identified primary site. Although patients with CUP have an unfavorable prognosis, treatment with site-specific therapies guided by clinical features, standard pathology, and molecular assays can improve overall survival. The 92-gene assay (CancerTYPE ID) is a gene expression-based classifier that helps identify the tissue of origin for metastatic cancers with unknown or uncertain diagnoses. This study reports the frequency of selected molecular aberrations of oncogenes, including KRAS, IDH1/2, BRCA1/2, and BRAF, in patients with CUP in the MOSAIC database to highlight potential treatment options. MOSAIC is a database of patients with CUP submitted for CancerTYPE ID testing and NeoTYPE biomarker testing. Tumor biopsy samples were analyzed by CancerTYPE ID for tumor type identification and further tested for molecular aberrations of oncogenes, including KRAS, IDH1/2, BRCA1/2, and BRAF. CancerTYPE ID identified a specific tumor type in 92.5% (2,929 of 3,168) of CUP cases in the MOSAIC database. The most commonly identified histological type was adenocarcinoma (75.4%), with pancreaticobiliary being the most common molecularly diagnosed cancer (24.9%). Aberrations in KRAS, IDH1/2, BRCA, and BRAF genes were identified in 18.8% (n = 597) of biopsies. A cancer-specific US Food and Drug Administration (FDA)-approved or investigational targeted therapy was potentially available for 24.6% (n = 147) of these patients. This retrospective analysis supports incorporating CancerTYPE ID into the evaluation for patients with CUP to help determine the tissue of origin and identify actionable genetic alterations. This approach may allow more patients with CUP to benefit from site-specific FDA-approved targeted therapies or enrollment into clinical trials.

Adjuvant ovarian function suppression (OFS) with oral endocrine therapy improves outcomes for premenopausal patients with hormone receptor-positive (HR+) breast cancer but adds adverse effects. A genomic biomarker for selecting patients most likely to benefit from OFS-based treatment is lacking. To assess the predictive and prognostic performance of the Breast Cancer Index (BCI) for OFS benefit in premenopausal women with HR+ breast cancer. This prospective-retrospective translational study used all available tumor tissue samples from female patients from the Suppression of Ovarian Function Trial (SOFT). These individuals were randomized to receive 5 years of adjuvant tamoxifen alone, tamoxifen plus OFS, or exemestane plus OFS. BCI testing was performed blinded to clinical data and outcome. The a priori hypothesis was that BCI HOXB13/IL17BR ratio (BCI[H/I])-high tumors would benefit more from OFS and high BCI portended poorer prognosis in this population. Settings spanned multiple centers internationally. Participants included premenopausal female patients with HR+ early breast cancer with specimens in the International Breast Cancer Study Group tumor repository available for RNA extraction. Data were collected from December 2003 to April 2021 and were analyzed from May 2022 to October 2022. Primary end points were breast cancer-free interval (BCFI) for the predictive analysis and distant recurrence-free interval (DRFI) for the prognostic analyses. Tumor specimens were available for 1718 of the 3047 female patients in the SOFT intention-to-treat population. The 1687 patients (98.2%) who had specimens that yielded sufficient RNA for BCI testing represented the parent trial population. The median (IQR) follow-up time was 12 (10.5-13.4) years, and 512 patients (30.3%) were younger than 40 years. Tumors were BCI(H/I)-low for 972 patients (57.6%) and BCI(H/I)-high for 715 patients (42.4%). Patients with tumors classified as BCI(H/I)-low exhibited a 12-year absolute benefit in BCFI of 11.6% from exemestane plus OFS (hazard ratio [HR], 0.48 [95% CI, 0.33-0.71]) and an absolute benefit of 7.3% from tamoxifen plus OFS (HR, 0.69 [95% CI, 0.48-0.97]) relative to tamoxifen alone. In contrast, patients with BCI(H/I)-high tumors did not benefit from either exemestane plus OFS (absolute benefit, -0.4%; HR, 1.03 [95% CI, 0.70-1.53]; P for interaction = .006) or tamoxifen plus OFS (absolute benefit, -1.2%; HR, 1.05 [95% CI, 0.72-1.54]; P for interaction = .11) compared with tamoxifen alone. BCI continuous index was significantly prognostic in the N0 subgroup for DRFI (n = 1110; P = .004), with 12-year DRFI of 95.9%, 90.8%, and 86.3% in BCI low-risk, intermediate-risk, and high-risk N0 cancers, respectively. In this prospective-retrospective translational study of patients enrolled in SOFT, BCI was confirmed as prognostic in premenopausal women with HR+ breast cancer. The benefit from OFS-containing adjuvant endocrine therapy was greater for patients with BCI(H/I)-low tumors than BCI(H/I)-high tumors. BCI(H/I)-low status may identify premenopausal patients who are likely to benefit from this more intensive endocrine therapy.

Advances in whole-slide imaging and artificial intelligence present opportunities for improvement in Pap test screening. To date, there have been limited studies published regarding how best to validate newer AI-based digital systems for screening Pap tests in clinical practice. In this study, we validated the Genius™ Digital Diagnostics System (Hologic) by comparing the performance to traditional manual light microscopic diagnosis of ThinPrep® Pap test slides. A total of 319 ThinPrep® Pap test cases were prospectively assessed by six cytologists and three cytopathologists by light microscopy and digital evaluation and the results compared to the original ground truth Pap test diagnosis. Concordance with the original diagnosis was significantly different by digital and manual light microscopy review when comparing across: (i) exact Bethesda System diagnostic categories (62.1% vs 55.8%, respectively, p = 0.014), (ii) condensed diagnostic categories (76.8% vs 71.5%, respectively, p = 0.027), and (iii) condensed diagnoses based on clinical management (71.5% vs 65.2%, respectively, p = 0.017). Time to evaluate cases was shorter for digital (M = 3.2 min, SD = 2.2) compared to manual (M = 5.9 min, SD = 3.1) review (t(352) = 19.44, p < 0.001, Cohen's d = 1.035, 95% CI [0.905, 1.164]). Not only did our validation study demonstrate that AI-based digital Pap test evaluation had improved diagnostic accuracy and reduced screening time compared to light microscopy, but that participants reported a positive experience using this system.

BCI (H/I) has been shown to predict extended endocrine therapy (EET) benefit. We examined BCI (H/I) for EET benefit prediction in NSABP B-42, which evaluated extended letrozole therapy (ELT) in patients with hormone receptor-positive breast cancer after 5 years of ET. A stratified Cox model was used to analyze RFI as the primary endpoint, with DR, BCFI, and DFS as secondary endpoints. Because of a nonproportional effect of ELT on DR, time-dependent analyses were performed. The translational cohort included 2,178 patients (45% BCI (H/I)-High, 55% BCI (H/I)-Low). ELT showed an absolute 10-year RFI benefit of 1.6% (P = 0.10), resulting in an underpowered primary analysis (50% power). ELT benefit and BCI (H/I) did not show a significant interaction for RFI (BCI (H/I)-Low: 10 years absolute benefit 1.1% [HR, 0.70; 95% confidence interval (CI), 0.43-1.12; P = 0.13]; BCI (H/I)-High: 2.4% [HR, 0.83; 95% CI, 0.55-1.26; P = 0.38]; Pinteraction = 0.56). Time-dependent DR analysis showed that after 4 years, BCI (H/I)-High patients had significant ELT benefit (HR = 0.29; 95% CI, 0.12-0.69; P < 0.01), whereas BCI (H/I)-Low patients were less likely to benefit (HR, 0.68; 95% CI, 0.33-1.39; P = 0.29; Pinteraction = 0.14). Prediction of ELT benefit by BCI (H/I) was more apparent in the HER2- subset after 4 years (ELT-by-BCI (H/I) Pinteraction = 0.04). BCI (H/I)-High versus BCI (H/I)-Low did not show a statistically significant difference in ELT benefit for the primary endpoint (RFI). However, in time-dependent DR analysis, BCI (H/I)-High patients experienced statistically significant benefit from ELT after 4 years, whereas (H/I)-Low patients did not. Because BCI (H/I) has been validated as a predictive marker of EET benefit in other trials, additional follow-up may enable further characterization of BCI's predictive ability.

The importance of developing a robust remote workforce in academic radiology has come to the forefront owing to several converging factors. COVID-19, as well as the abrupt transformation it precipitated in terms of how radiologists work, has been the biggest impetus for change; concurrent factors such as increasing examination volumes and radiologist burnout have also contributed. How to best advance the most desirable and favorable aspects of remote work while preserving an academic environment that fulfills the tripartite mission is a critical challenge that nearly all academic institutions face today. In this AJR Expert Panel Narrative Review, we discuss current challenges in academic radiology, including effects of the COVID-19 pandemic, from three perspectives-those of the radiologist, the learner, and the health system-and address the following topics: productivity, recruitment, wellness, clinical supervision, mentorship and research, educational engagement, radiologist access, investments in technology, and radiologist value. Throughout, we focus on the opportunities and drawbacks of remote work, to help guide its effective and reliable integration into academic radiology practices.