tbuckley/cabot-search huggingface dataset:
Radiofrequency ablation without excision for breast cancer: 5-year results of ipsilateral breast tumor recurrence-free survival in the RAFAELO study (NCCH1409).
Authors: Takayuki Kinoshita, Shin Takayama, Masato Takahashi, Tomomi Fujisawa, Naohito Yamamoto, Tadahiko Shien, Manabu Futamura, Mitsuya Ito, Mina Takahashi, Kenjiro Aogi, Kenichi Watanabe, Tatsuya Onishi, Masayuki Yoshida, Hitoshi Tsuda, Taro Shibata, EIko Yorikane, Keiko Ohata
Journal: Journal of Clinical Oncology (2024)

578 Background: The RAFAELO study is designed to evaluate the safety and efficacy of breast radiofrequency ablation, enabling women between 20 and 79 years of age with small breast cancers to benefit from a nonsurgical treatment and to avoid the associated surgical risks. Methods: The RAFAELO study is a prospective, multi-center, single-arm, confirmatory trial including women between 20 and 79 years of age with unifocal, MRI and ultrasound-visible invasive ductal carcinoma or ductal carcinoma in situ size 1.5 cm or smaller diagnosed by preoperative core-needle biopsy, and classified as any grade, any hormone receptor (HR) status, and any human epidermal growth factor receptor 2 (HER2) status. The primary endpoint was Ipsilateral breast tumor recurrence (IBTR) -free survival at 5 years. e planned sample size was set at 372 considering patients lost to follow-up to achieve the power of 80% with the one-sided alpha of 5%, the expected 5-year IBTR-free survival of 93.7%, and the threshold of 90%. The IBTR-free survival time was estimated using the Kaplan-Meier method. Results: Between August 2013 and November 2017, 372 patients were enrolled in this study. A total of 370 patients received successful radiofrequency ablation per protocol and 353 patients were included for the efficacy analysis. The median age was 55 years (range, 32-78 years). With median follow-up period of 5 years, IBTR was observed in 2 patients among 353 patients. The 5-year IBTR-free survival was 98.6% (90% CI 97.1%-99.3%) and the primary endpoint was met as its lower limit of 90% CI exceeded the threshold (90%). As device-related adverse events during radiofrequency ablation, burns were observed in 4 (1.1%) for Grade 1 and 3 (0.8%) for Grade 2 among 370 patients. No severe device-related adverse events were reported. Conclusions: Breast radiofrequency ablation presents a promising alternative to surgery while offering the benefits of a minimally invasive procedure with minimal risks. Breast cancer patients in Japan can now avail themselves of this minimally invasive treatment that’s subsidized by public insurance. Further analysis within this clinical trial or registry is needed to confirm radiofrequency ablation as a viable alternative to surgical excision for breast cancer patients in the point of cosmetic results and patient’s satisfaction. Clinical trial information: jRCTs032180229 .
Adjunctive statistical standardization of quantitated adjuvant ER and PgR in CCTG MA.27.
Authors: Judy-Anne Williams Chapman, Jane Bayani, Sandip SenGupta, John MS Battlett, Tammy Piper, Mary Anne Quintayo, Shakeel Virk, Paul E Goss, James N. Ingle, Matthew James Ellis, George W. Sledge, George Thomas Budd, Manuela Rabaglio, Vered Stearns, Edith A. Perez, Karen A. Gelmon, Timothy Joseph Whelan, Lois E. Shepherd, Bingshu E. Chen, Karen J Taylor
Journal: Journal of Clinical Oncology (2024)

567 Background: We proposed adjunctive statistical standardization of quantitated ER and PgR to improve inter-laboratory comparability of biomarker results and therapeutic management of breast cancer. Methods: We utilized CCTG MA.27 (NCT00066573), an adjuvant phase III trial of exemestane versus anastrozole in postmenopausal women with ER+ and/or PgR+ tumours. IHC ER and PgR HSCORE and % positivity (%+) were centrally assessed by machine image quantitation, and each statistically standardized to mean of 0, standard deviation of 1 following Box-Cox variance stabilization transformations of square for ER; for PgR, 1.) natural logarithm (0.1 added to 0 HSCOREs and 0 %+), 2.) square root. The primary endpoint was STEEP distant disease-free survival (DDFS) at the longest trial follow-up of median 4.1 years; a secondary endpoint was event-free survival (EFS). Survival was described with Kaplan-Meier plots and tested with the univariate Wilcoxon (Peto-Prentice) test statistic. We examined cut-points at standard deviations about mean of 0 (&lt;-1; (-1,0]; (0,1]; &gt;1) and explored single cut-points. Cox multivariate regressions were adjusted for age, T and N stage, grade, lymphovascular invasion, treatment, and baseline patient demographics; 2-sided Wald tests had nominal significance if p&lt;0.05. Results: Of the 7576 women accrued, 3048 women had machine-quantitated image analysis results: 2900 (95%) for ER; 2726 (89%) for PgR. Statistically standardized HSCORE and %+ units differentiated both univariate DDFS and EFS; DDFS was significantly different by ER levels (p&lt;0.001) and PgR levels (p&lt;0.001). In multivariable assessments, ER HSCORE and %+ were not significantly associated (p=0.52-0.88) with the DDFS primary endpoint in models with PgR, while higher PgR HSCORE and %+ had significantly better DDFS (p=.001, in all instances) in models with ER. Conclusions: DDFS was superior for patients with higher ER and PgR standardized units compared with those with HSCOREs and %+ &lt;-1. The adjunctive statistical standardization of ER and PgR performed here is similar to that mandated for clinical practice by the World Health Organization for BMD. [Table: see text]
PARPi eligibility amongst women with newly diagnosed invasive breast cancer enrolled in the GREAT universal genetic testing study.
Authors: Stephanie M. Wong, Zoulikha Rezoug, Stephanie P Totten, David M Szlachtycz, Adrienne M Atayan, Kristen Mohler, Sophie Albert, Leila Feng, Brianna Lemieux Anglin, Zhen Shen, Nancy Hamel, Nicholas Meti, Khashayar Esfahani, Dawn Anderson, Jean-Francois Boileau, Ipshita Prakash, Mark Basik, Sarkis H. Meterissian, George Chong, William D. Foulkes
Journal: Journal of Clinical Oncology (2024)

531 Background: Poly(ADP-ribose) polymerase inhibitors (PARPi) are approved for the treatment of BRCA-associated metastatic and high-risk early breast cancer. Recent guidelines have endorsed the use of BRCA1/2 testing for all patients with breast cancer who may be eligible for PARPi therapy, however it is unclear what proportion of patients are impacted by these recommendations. Methods: We sought to evaluate pre-test and post-test eligibility for PARPi in women aged &gt;18 years old with a first diagnosis of stage I-IV invasive breast cancer who enrolled in a prospective, multicentered universal genetic testing study between 2019-2022. All enrolled patients underwent pre-test counselling and an obligatory primary panel of BRCA1, BRCA2, and PALB2 at the time of diagnosis. Candidacy for PARPi was determined by biologic subtype, clinical stage, pathologic stage, and/or response to neoadjuvant chemotherapy. Results: Of 1017 patients referred for the study, 805 were eligible and 729 (90.6%) consented to participate. The median age at diagnosis was 53 years (IQR, 45-62 years) and 96.2% had stage I-III disease. Overall, 32 (4.4%) patients had a germline pathogenic variant (GPV) in BRCA1/2, and 7 (1.0%) had a GPV in PALB2. Of 112 (15.4%) patients with triple negative breast cancer (TNBC), 19.6% had a GPV in BRCA1/2 or PALB2, whereas in 487 (66.8%) with ER+HER2- disease, 2.9% had a GPV (p&lt;0.001). Among the 112 patients with TNBC, 64 (57%) were candidates for PARPi pre-genetic testing, and 12 (10.7%) remained eligible due to a GPV in BRCA1/2. Among the 487 patients with ER+HER2- breast cancer, 37 (5.1%) were candidates for PARPi pre-genetic testing, and 1 (0.2%) remained eligible due to a BRCA1 GPV identified on testing. All patients who were PARPi eligible were &lt;65 years of age or &gt;65 years with TNBC. Conclusions: In women with newly diagnosed invasive breast cancer, approximately 14% are candidates for PARPi prior to genetic testing, and 1.8% remain PARPi eligible due to a confirmed pathogenic variant in BRCA1/2. Consequently, genetic testing impacts systemic therapy decisions for PARPi in 10% of TNBC patients and fewer than 1% of women with ER+HER2- breast cancer. [Table: see text]
The landscape of the intestinal microbiome amongst patients with newly diagnosed invasive breast cancer (BC) and ductal carcinoma in situ (DCIS).
Authors: Sarah L Sammons, Thomas Kuntz, Melissa E. Hughes, Xochitl Morgan, Adrienne Gropper Waks, Ana Christina Garrido-Castro, Molly DiLullo, Jennifer A. Ligibel, Esther R. Ogayo, Julia Giordano, Sean Ryan, Tasnim Rahman, ROMUALDO BARROSO-SOUSA, Alyssa R. Martin, Casey O'Quinn, Tonia Parker, Nancy U. Lin, Elizabeth A. Mittendorf, Sara M. Tolaney
Journal: Journal of Clinical Oncology (2024)

563 Background: The gut microbiota has been recognized as a master modulator of the immune system and has been associated with outcomes among cancer patients. There is a paucity of knowledge surrounding the landscape of the gut microbiome in BC pts with invasive and non-invasive disease or how it can affect response to systemic therapy. We sought to define the landscape of the gut microbiome in invasive BC and DCIS patients and evaluate differences by subtype. Methods: Prospective clinical trials (NCT04425018 and NCT02752685) and institutional registries enrolling patients with DCIS and stage I-III invasive BC at a single institution collected baseline stool samples prior to initiation to systemic therapy. Samples underwent whole-genome metagenomic sequencing (2 x 150 bp, Illumina NovaSeq) and data were processed into taxonomic (species) abundances using MetaPhlAn4. Associations between metadata and ecological features (alpha and beta diversity) were tested with ANOVA and PERMANOVA. Individual species were tested for association as univariable linear models on normalized, log-transformed relative abundances, with p-values FDR adjusted for multiple comparisons, using MaAsLin2. Results: 278 baseline samples were analyzed: 43 DCIS, 101 TNBC, 54 HR+/HER2-, 24 HR-/ HER2+, 37 HR+/HER2+. Most patients were age &gt;50 (71%) and white (83%). No significant differences were observed in baseline stool microbiomes between invasive BC and DCIS pts for alpha (p = 0.185) or beta (p= 0.476) diversity, nor for individual microbial species. HR+/HER2+ BC had a unique microbiome profile with significantly different beta diversity (p = 0.005), with six associated species (q &lt; 0.25). No other associations with invasive BC subtype were observed. Bacteroides ovatusabundance was significantly different between Stage I and III BC pts (p = 0.0003, q = 0.116). Several taxa were associated with age at diagnosis. Conclusions: No significant differences were observed in baseline stool microbiome composition between patients with invasive breast cancer and DCIS. HR+/HER2+ BC microbiome composition was distinct from other subtypes, and a significant difference in Bacteroides ovatus abundance emerged between Stage I and III pts, suggesting potential microbial associations with cancer subtypes and progression.
Prediction of pCR with pretreatment MRI radiomics in triple negative breast cancer treated with neoadjuvant chemo-immunotherapy.
Authors: Toulsie Ramtohul, Victoire Lepagnay, Claire Bonneau, Maxime Jin, Emmanuelle Menet, Juliette Sauge, Enora Laas, Emanuela Romano, Diana Bello, Fatima Mechta-Grigoriou, Francois Clement Bidard, Adriana Langer, Caroline Malhaire, Luc Cabel, Anne Tardivon
Journal: Journal of Clinical Oncology (2024)

589 Background: Neoadjuvant chemo-immunotherapy (NACI) has become the new standard of care for stage II-III triple-negative breast cancers (TNBC). This approach is linked to high rates of pathological complete responses (pCR), yet factors influencing responses to this regimen remain poorly characterized. Our objective was to investigate whether pretreatment MRI perfusion-based radiomics could serve as a predictive tool for pCR to NACI in patients with TNBC. Methods: This prospective study enrolled women with early-stage TNBC undergoing NACI at two different centers from August 2021 to July 2023. Tumor segmentation and pharmacokinetic analyses of pretreatment multi-vendor dynamic-contrast-enhanced (DCE)-MRI were conducted using the standard perfusion Tofts model. Radiomics features were extracted from the K trans , V e , and Slope max maps. Unsupervised correlation analysis of reproducible features and least absolute shrinkage and selector operation were employed to construct a radiomics score. The performance of the radiomic score was evaluated using the area under the receiver operating characteristic curve (AUC). A multivariable logistic regression model was employed to establish a multimodal nomogram. Performance metrics included discrimination, calibration, and clinical usefulness, assessed on the external validation set. Results: The training set comprised 112 patients from center 1 (mean age, 52 years ± 11 [SD]), and the external validation set included 83 participants from center 2 (mean age, 49 years ± 11 [SD]). Overall, the pCR rate was 70% (137/195). In the validation set, the radiomic score achieved an AUC of 0.80 (95% CI: 0.70, 0.89) for predicting pCR. A nomogram incorporating the radiomics score, tumor grade, and Ki67 yielded an AUC of 0.86 (95% CI: 0.78, 0.94) and demonstrated superior predictive efficacy compared to the histological prediction model (AUC, 0.76 [95% CI: 0.66, 0.87]; P = .02) in the validation set. Calibration curves of the nomogram exhibited good concordance between predicted and observed pCR in both sets. The radiomic score correlated with tumor size, washout enhancement curve, androgen receptor (AR), and combined positive score (CPS) PD-L1 levels (all P &lt; 0.05). Conclusions: PretreatmentMRI-based radiomics is a reliable predictor of pCR to NACI and might help customizing treatment choice for early-stage TNBC patients.
Artificial intelligence algorithms for recurrence risk score prediction in early-stage breast cancer: A multicenter study of 437 cases.
Authors: Ertugrul Bayram, Gul Basaran, Erhan Gokmen, Nil Molinas Mandel, Basak Oyan, Necdet Uskent, Bulent Karagoz, Yesim Yildirim, Sinan Karaaslan, Emine Nilufer Guler, Yesim Eralp, ÖZLEM ER, Aziz Karaoglu, Seref Komurcu, Burak Mete, Fahriye Gemci, Turgay Ibrikci, Semra Paydas
Journal: Journal of Clinical Oncology (2024)

545 Background: In non-developed and developing countries, the 21 gene assay (Oncotype DX) test is expensive and not covered by private and social health insurance, which limits its use. However, clinicians need this test for treatment prediction in early stage breast cancer. Therefore, we aimed to predict the risk of recurrence in patients using artificial intelligence (AI) algorithms. Methods: This study was conducted with 437 early-stage breast cancer patients whose recurrence risk scores were determined by 21 Oncotype DX and were followed in 14 oncology centers in Turkey between 2012 and 2022. The optimum cut-off for the 21 gene assay risk score (RS) was found by ROC analysis using the patients' recurrence information (scale of RS ≥17: high risk, &lt;17: low risk). In artificial intelligence prediction models, patients with a 21 gene assay risk score ≥17 were considered high risk. Additionally, algorithm predictions were made within other cut-offs recommended in the literature (&lt;10, 11-25, &gt;25). Python programming language was used in the analyses. The Random Forest (RF), Logistic regression (LG) and K-Nearest Neighbors (KNN) were used as AI prediction algorithms. Results: The study analyzed 437 women with an average age of 49.49±10.30. During the follow-up period from 2012 to 2022, 7.1% of the patients experienced a recurrence. AI prediction algorithms which were RF, LG, and KNN generated confusion matrices for two classes, with a threshold of 17 and 25. The algorithms were then applied to the dataset with risk score thresholds of 1-10, 11-25, and greater than 25, resulting in three classes. The dataset was analyzed using recall, precision, and F1 score values. The results showed that RF had the most accurate predictions for RS = 17 (Table). Conclusions: The use of artificial intelligence algorithms in predicting the risk of recurrence in early stage breast cancer is promising in determining treatment decisions for clinicians who cannot access genetic results. [Table: see text]
An MRI-based multi-parameter clinical decision support system (NeoMDSS) for early prediction of pathological complete response after the first cycle of neoadjuvant therapy in breast cancer: A multi-center prospective observational cohort study.
Authors: Liulu Zhang, Zhaowen Zhu, Yuanqi Chen, Yidan Wu, Yongcheng Zhang, Guolin Ye, Wei Li, Kun Wang
Journal: Journal of Clinical Oncology (2024)

592 Background: Currently, there is no reliable and convenient way to predict response rates in early stages of neoadjuvant therapy (NAT) for breast cancer, which can affect the prognosis of non-responsive patients. Methods: In this prospective multi-center observational study, eligible participants who underwent NAT followed by radical surgery were recruited. All patients underwent magnetic resonance imaging (MRI) examination before and after the first cycle of NAT (1 st -NAT). We analyzed the effects of clinicopathological features, such as histological grade, hormone receptor (HR) and HER2 status, Ki-67 expression, blood cell analysis, and MRI parameters including the reduction ratio of tumor diameter (ΔD%), apparent diffusion coefficient (ADC), and early enhancement ratio (EER) on NAT. By incorporating independent influencing factors, we developed a multiparametric clinical decision support system (NeoMDSS) model based on a retrospective cohort and validated its accuracy in both a prospective single-center internal cohort and a prospective multi-center external cohort. Relevant clinical trial details can be found under the identifier NCT04909554. Results: A total of 301 breast cancer patients were enrolled between January 2019 and December 2023, including 140 patients in the training cohort, 120 patients in the internal validation cohort, and 41 patients in the external validation cohort. The NeoMDSS model demonstrated excellent performance in predicting pathological complete response (pCR) at 1 st -NAT, with an AUC of 0.874 (95% CI 0.813-0.935) in the training cohort, 0.845 (95% CI 0.771-0.919) in the internal validation cohort, and 0.867 (95% CI 0.742-0.992) in the external validation cohort. The NeoMDSS model exhibited a sensitivity, specificity, and accuracy of 80.0%, 81.3%, and 80.5%, respectively, in the internal validation cohort, and 87.0%, 83.3%, and 85.4%, respectively, in the external validation cohort. Calibration curves and decision curve analysis (DCA) further supported the clinical value of the NeoMDSS model. To facilitate the clinical application of the NeoMDSS model, a nomogram and an online website ( www.gdphneomdss.com ) were developed to calculate the probability of pCR, which the sensitivity, specificity, and accuracy of the NeoMDSS model were all 80.0% in the internal validation cohort and 80.0%, 81.3%, and 80.5% in the external validation cohort. Conclusions: The NeoMDSS model serves as a convenient tool in clinical practice at 1st-NAT to calculate and accurately predict the probability of pCR after NAT. This assists clinicians in determining whether to modify the treatment plan and improving personalized treatment for patients with poor response to NAT. Clinical trial information: NCT04909554 .
Short-term risk of recurrence in patients (pts) with HR+/HER2− early breast cancer (EBC) treated with endocrine therapy (ET) in randomized clinical trials (RCTs): A meta-analysis.
Authors: Giuseppe Curigliano, Eva Ciruelos, Kevin Kalinsky, David Proudman, Dave Nellesen, Priscilla Lopez, Samantha Kaufhold, Olivia Shane, Adrienne Kwok, Vaidyanathan Ganapathy, Adaeze Q. Amefule, Agnes Lteif, Peter A. Fasching, Erika P. Hamilton
Journal: Journal of Clinical Oncology (2024)

541 Background: ET ± chemotherapy (CT) has been standard of care for HR+/HER2− EBC for ≈20 yrs, yet pts continue to experience disease recurrences up to decades after completing adjuvant (adj) treatment (tx). There is little information on early recurrences (≤5 yrs) in the more recent tx landscape, which includes optimized local tx and improved systemic tx. This meta-analysis of ET arms in RCTs in pts with HR+/HER2− EBC seeks to fill this gap by evaluating 3- and 5-yr risk of recurrence (RoR). Methods: A systematic literature review identified RCTs that met study criteria: phase 3 trials that included pts with HR+/HER2− EBC who received 5 yrs of adj ET (aromatase inhibitors ± ovarian suppression or tamoxifen) ± CT with invasive disease-free survival (iDFS) or disease-free survival (DFS) reported at 3 and/or 5 yrs post tx randomization. Neoadj or extended ET trials were excluded. Survival curves were digitized for iDFS estimates. Meta-analysis of proportions, using a random-effects model with double arcsine transformation, was used to evaluate pooled 3-yr RoR (100 − iDFS/DFS rate) for all pts, by lymph node (LN) status (N0: 0, N1: 1-3, N+ any positive LN), and 5-yr RoR where available. The 3-yr RoR across ET arms in a subset of only adj cyclin-dependent kinase 4/6 inhibitor (CDK4/6i) trials was evaluated given the recency of these RCTs. Results: RoR estimates were obtained from 14 trials (across 23 distinct ET-only arms), including 4 adj CDK4/6i trials that met study criteria; studies included pts with stage I-III disease ± nodal involvement. Pooled 3- and 5-yr RoR for the overall HR+/HER2− EBC pt population regardless of LN/stage were 7% and 12%, respectively. In trials with nodal status subgroups, pooled 3-yr RoR was 5% for N0, 6% for N1, and 13% for N+ pts. In a sensitivity analysis excluding cohorts with low to medium risk scores based on genomic testing, pooled 3-yr RoR estimates for N0 and N1 subgroups were 7% and 10%, respectively. Meta-analysis across ET arms of CDK4/6i trials revealed 3-yr RoR of 15% (all stage I-III) and 10% (N0). Conclusions: Despite the potential for late recurrence of HR+/HER2− EBC, short-term RoR is considerable and accumulates over time for these pts treated with ET ± CT in the adj setting. These findings highlight the unmet need for tolerable tx escalation among at-risk pts, including node-positive and select N0 pts with high-risk features.