TPS649 Background: While advancements in early-stage breast cancer (ESBC) treatment have improved survival, disease recurrence and therapeutic resistance remain significant challenges. Current biomarkers guide initial treatment, yet novel diagnostics are required to improve risk stratification and recurrence prediction. Circulating tumor DNA (ctDNA) offers a non-invasive method for detecting minimal residual disease (MRD), potentially enabling the identification of molecular relapse before radiographic progression. However, deeper validation of ctDNA kinetics across the treatment continuum is needed to inform precision oncology strategies in ESBC. Additionally, the logistical complexity of tissue-dependent assays presents a barrier to scalability. In order to address these gaps, the purpose of the GEMINI-BREAST study is to evaluate a methylation-based, tissue-free ctDNA assay (Tempus xM) and integrate this with comprehensive clinical and multi-omic data to determine prognostic utility for invasive disease-free survival (iDFS) and long term outcomes in patients with ESBC. Methods: GEMINI-BREAST (NCT07211178) is a prospective, non-interventional, multi-center study enrolling up to 900 participants with ESBC treated with curative intent. Eligible patients enroll into one of three analytical cohorts (n ~ 300 each): Cohort 1 enrolls pre-neoadjuvant therapy (high-risk HR+/HER2- [Stage II-III], HER2+ [Stage II-III], or TNBC [Stage I-III]); Cohort 2 enrolls post-surgery/pre-adjuvant therapy (same subtypes, no evidence of disease); and Cohort 3 enrolls long-term survivors (high-risk HR+/HER2- [Stage II-III], ≥ 5 years since diagnosis). Longitudinal blood samples for ctDNA analysis and archival tissue for NGS are collected at standard-of-care intervals, including, as applicable: pre-neoadjuvant, neoadjuvant on-treatment (weeks 3, 6, 12) and post-treatment, post-surgical/pre-adjuvant, adjuvant on-treatment (every 3 months), end of definitive therapy, surveillance for up to 5 years (every 3-6 months for Cohorts 1/2; every 6-12 months for Cohort 3), and at progression/recurrence. The primary objective is to evaluate the association between ctDNA dynamics (Cohort 1) or MRD status (Cohort 2/3) and iDFS, with secondary endpoints assessing overall survival, lead time to recurrence, and the predictive value of ctDNA clearance for pathological complete response. Recruitment is currently ongoing at U.S. sites. Clinical trial information: NCT07211178 .

Chemotherapy-induced peripheral neuropathy (CIPN) is a common and serious adverse effect of chemotherapeutic agents such as taxanes, platinum compounds, and vinca alkaloids. Efforts to prevent and treat CIPN are impeded by an incomplete understanding of its pathogenesis. Recently, the gut microbiota has been causally linked to CIPN in rodent models. However, human studies exploring this connection are limited. Here, in a cohort of 70 patients with early-stage breast cancer, relationships between disruptions in the gut microbiota during chemotherapy and both participant CIPN symptoms and general pain symptoms were investigated. Study participants provided fecal samples (for 16S rRNA sequencing and targeted metabolomics), blood samples, and sensory symptom information during the three days prior to their first and their final chemotherapy (including a taxane drug) infusions. Sensory neuropathy symptoms increased during treatment, as did circulating levels of neurofilament light chain (NFL), a putative biomarker of CIPN. Decreases in microbiota alpha diversity during chemotherapy were associated with worse neuropathy symptoms during treatment, along with worsening of general pain, after controlling for pre-treatment baseline symptoms. Larger shifts in beta diversity from baseline to last infusion also coincided with more severe neuropathy symptoms. Bacterial producers of short-chain fatty acids were decreased in participants with neuropathy symptoms at the final chemotherapy infusion. Furthermore, decreases in fecal levels of short-chain fatty acids during treatment were related to worse neuropathy symptoms, suggesting a potential mechanism by which gut microbiota alterations could influence CIPN. Collectively, these findings corroborate preclinical work linking the gut microbiota to CIPN and provide evidence of potential microbiota involvement in general pain symptoms as well. Larger confirmatory studies in the future could support microbiota-targeted interventions for CIPN, such as fecal microbiota transplants or dietary interventions.

Invasive lobular carcinoma (ILC) accounts for 10-15% of breast cancers. Despite favorable responses to anti-estrogen therapy, the dissemination of cancer cells and resistance to therapies are significant risks for patients with ILC. Late recurrences are prevalent in ILC, suggesting that disseminated tumor cell (DTC) dormancy may be a mechanism preceding their late overt growth into metastatic lesions. Herein, we investigated the relationship between anti-estrogen resistance and dormancy through multidimensional, micro-compartmentalized in vitro models. The bioengineered platforms recapitulated the morphological characteristics of ILC and highlighted its distinction from invasive ductal carcinoma (IDC). Inducing a reversible dormant phenotype revealed epigenetic changes and enhanced chemical and mechanical sensing of anti-estrogen-resistant ILC cells to the substrate surface, with p27Kip1 signaling playing a central role. We propose this platform as a high-throughput method for investigating ILC dormancy and its manifestation using a simplified, expedited approach.

ABSTRACT Purpose Tumor genomic testing (TGT) is standard-of-care for most patients with advanced/metastatic cancer. Despite established guidelines, patient education prior to TGT is frequently omitted. The purpose of this study was to evaluate the impact and durability of a concise 3-4 minute video for patient education prior to TGT in community versus academic sites and across cancer types. Patients and Methods Patients undergoing standard-of-care TGT were enrolled at a tertiary academic institution in three cohorts: Cohort 1-breast cancer; Cohort 2-lung cancer; Cohort 3-other cancers. Cohort 4 consisted of patients with any cancer type similarly undergoing SOC TGT at one of three community cancer centers. Participants completed survey measures prior to video viewing (T1), immediately post-viewing (T2), and after return of TGT results (T3). Outcome measures included: 1) 10-question objective genomic knowledge/understanding (GKU); 2) 10-question video message-specific knowledge (VMSK); 3) 11-question Trust in Physician/Provider (TIPP); 4) perceptions regarding TGT. Results A total of 203 participants completed all survey timepoints. Higher baseline GKU and VMSK scores were significantly associated with higher income and greater years of education. For the primary objective, there was a significant and sustained improvement in VMSK from T1:T2:T3 (P overall p<0.0001), with no significant change in GKU (p=0.41) or TIPP (p=0.73). This trend was consistent within each cohort (all p≤0.0001). Results for four VMSK questions significantly improved, including impact on treatment decisions, incidental germline findings, and insurance coverage of testing. Conclusions A concise, 3-4 minute, broadly applicable educational video administered prior to TGT significantly and sustainably improved video message-specific knowledge in diverse cancer types and in academic and community settings. This resource is publicly available at http://www.tumor-testing.com , with a goal to efficiently educate and empower patients regarding TGT while addressing guidelines within the flow of clinical practice.

Background Fatigue is a common and debilitating side effect of chemotherapy, negatively affecting treatment adherence and survival. Chemotherapy alters gut microbiome composition, and accumulating evidence suggests that gut microbes contribute to chemotherapy-induced fatigue. Because the gut microbiome is modifiable through targeted interventions, such as fecal microbiota transplantation (FMT), microbiome modulation has emerged as a potential strategy to mitigate treatment-related toxicities. To understand the impact of FMT interventions across the gut-brain axis, studying rodent chemotherapy models that simultaneously capture behavioral side effects and gastrointestinal pathology is warranted. Methods Patient-reported fatigue and diarrhea were assessed in breast cancer patients before and during chemotherapy (n = 67). In parallel, mice were treated with chemotherapy (5-fluorouracil [5-FU] or paclitaxel) with or without FMT derived from pre-chemotherapy fecal material. Outcomes included fatigue (in-cage locomotion and voluntary wheel running), gut microbiome composition (16S rRNA sequencing), intestinal and brain gene/protein expression (RT-qPCR, single-cell RNA sequencing, and/or multiplex electrochemiluminescence assay), and circulating inflammatory markers. Results In patients, increased fatigue during chemotherapy was associated with worse diarrhea and shifts in gut microbiome composition. In mice receiving 5-FU, FMT produced mild-to-moderate benefits, most notably preserving body mass, with milder and transient benefits for fatigue. FMT partially normalized gut bacterial taxa, reduced 5-FU-induced colonic Il1b expression, and prevented chemotherapy-related increases in brain Aqp4. FMT did not attenuate other inflammatory effects induced by 5-FU or paclitaxel. Conclusion These findings are consistent with a role for gut microbes in chemotherapy-induced fatigue and suggest that FMT is not universally beneficial, with effects varying by chemotherapy drug.

TPS623 Background: Approximately 50% of newly diagnosed invasive breast cancers are stage 1, with the majority being ER/PR-positive, HER2-negative. Genomic assays such as the Oncotype DX have identified patients (pts) with reduced risk of distant metastasis and without benefit from chemotherapy added to endocrine therapy, freeing them from excess toxicity. Genomic assays are also recognized as prognostic for in-breast recurrence (IBR) after BCS and could similarly allow de-escalation of adjuvant radiotherapy (RT). Reducing overtreatment is of interest to pts, providers, and payers. Methods: We hypothesize that BCS alone is non-inferior to BCS plus RT for in-breast recurrence and breast preservation in women intending endocrine therapy (ET) for stage 1 invasive breast cancer (ER &/or PR positive, HER2-negative with an Oncotype DX Recurrence Score [RS] of ≤18). Stratification is by age (<60; ≥60), tumor size (≤1 cm; >1-2cm), and RS (<11, 11-18). Pts are randomized post-BCS to Arm 1 with breast RT using standard methods (hypo- or conventional-fractionated whole breast RT with/without boost, or APBI) with ≥5 yrs of ET (tamoxifen or AI) or Arm 2 with ≥5 yrs of ET (tamoxifen or AI) alone. The specific regimen of ET in both arms is at the treating physician’s discretion. Eligible pts are stage 1: pT1 (≤2 cm), pN0, age ≥50 to <70 yrs, s/p BCS with negative margins (no ink on tumor), s/p axillary nodal staging (SNB or ALND), ER &/or PR positive (ASCO/CAP), HER2-negative (ASCO/CAP), and Oncotype DX RS of ≤18 (diagnostic core biopsy or resected specimen). Primary endpoint is in-breast recurrence (invasive breast cancer or DCIS). Secondary endpoints are breast conservation rate, invasive in-breast recurrence, relapse-free interval, distant disease-free survival, overall survival, patient-reported breast pain, patient-reported worry about recurrence, and adherence to ET. We assume a clinically acceptable difference in IBR of 4% at 10 yrs to judge omission of RT as non-inferior (10-yr event-free survival for RT group is 95.6% vs 91.6% for the omission of RT group). BR007 is powered to detect non-inferiority with 80% power and a one-sided α=0.025, assuming that there would be a ramp-up in accrual in the first two years (leveling off in Yrs 3-5); 1,670 pts (835 per arm) are required for randomization. Conservative loss to follow-up is 1% per yr. Some of the T1a pts screened may have Oncotype DX scores >18, making them ineligible for the study. In the accrual process, 1,714 pts will be required to register to ensure that our final randomized cohort is 1,670 pts. Current accrual (02-08-2023) is 370 screened and 323 randomized (~87% of predicted accrual). Clinical trial information: NCT04852887 .

Purpose:To develop and validate a multimodal recurrence-risk model integrating histology, genomic testing, and clinical variables. Methods:We developed AI-Path, a whole-slide image biomarker for recurrence prediction trained in CALGB 9344, and validated it in three independent cohorts: TAILORx, a multi-site Chicago cohort, and the MDX-BRCA cohort. We then integrated AI-Path with Oncotype DX Recurrence Score (RS), tumor size, and nodal status into a Cox model, PathClinRS, fit using 60% of cases from TAILORx, with the remaining 40% held out for validation. The primary end point was distant recurrence-free interval. Performance was assessed using Harrell's concordance index (C-index) and Kaplan-Meier analyses. Results:A total of 12,418 patients were included. In TAILORx, AI-Path outperformed RS for distant recurrence (C-index, 0.682 vs 0.647; P = .038), driven by superior prediction of late recurrence (0.656 vs 0.567; P < .001). In node-negative disease, PathClinRS outperformed RSClin in the TAILORx fitting (0.72 vs 0.70; P = .016) and validation sets (0.74 vs 0.70; P = .004). In node-positive disease, PathClinRS outperformed RSClinN+ in Chicago (0.94 vs 0.74; P < .001) and MDX-BRCA (0.71 vs 0.66; P = .004) cohorts. Compared with NATALEE eligibility, PathClinRS identified nearly twice as many high-risk node-negative patients while maintaining a comparable 10-year distant recurrence risk (16.7% vs 16.6% per NATALEE eligibility in TAILORx fitting; 21.0% vs 19.4% in TAILORx validation). PathClinRS identified 68% of intermediate risk premenopausal patients as low-risk with no evidence of chemotherapy benefit, compared to only 36% identified as low risk by standard clinicopathologic criteria. Conclusion:Digital histopathology provides prognostic information complementary to genomic assays and has the potential to personalize therapy beyond existing clinicogenomic tools.

Stromal tumor infiltrating lymphocytes (sTILs), quantified via hematoxylin and eosin (H&E) tumor slides, are associated with improved response to chemotherapy and better overall survival (OS) in triple-negative breast cancer (TNBC). Digitization of H&E slides offers the opportunity for image-based computational approaches to enumerate sTILs. We describe QuTILs, a research-based TIL enumeration approach using a multilayer perceptron-based framework trained on open-source H&E images using the QuPath software and executed on a standard computer. QuTILs was applied to H&E images from two phase III TNBC clinical trials, CALGB 40502 and 40603 (total n = 462 patients). In Cox proportional hazards models, QuTILs showed significant univariate association in CALGB 40502, with higher TIL% associated with reduced hazard (HR: 0.75, 95% CI: 0.63-0.91), which remained significant in multivariable models and validation CALGB 40603 TNBC clinical trial. In summary, QuTILs provides a computationally efficient, open-source workflow for sTIL identification from digital H&E images for research application.

Combination axillary lymph node dissection (ALND) and regional nodal irradiation (RNI) poses the greatest risk for breast cancer-related lymphedema. Despite interest in minimizing combination therapy (i.e., ALND+RNI) to avoid complications, concern remains over the oncologic safety of de-escalation in younger populations. This retrospective analysis explores the association between age and receipt of ALND+RNI to gain insight into patterns of axillary management. Using the National Cancer Database (2018–2020), age-based differences in ALND+RNI among patients with stage I–III breast cancer undergoing surgery were examined. Patient and treatment characteristics were compared by age (<45, 45–64, and ≥65 years). Multivariable regression assessed associations between age and treatment, adjusting for clinical factors. Among 439,790 patients, 7.5

e12531 Background: Invasive lobular cancer (ILC) is biologically and clinically distinct from invasive ductal carcinoma (IDC). Prior studies evaluating CT benefit across these histologic subtypes yielded conflicting results, highlighting a knowledge gap. We studied the impact of adjuvant CT on overall survival (OS) and identified factors associated with CT receipt in patients (pts) with early-stage HR+/HER2– ILC and IDC. Methods: Data was obtained from the National Cancer Database for pts who had surgical resection of ILC or IDC of the breast between 2010 and 2021. Subgroup analyses were conducted by CT receipt, Oncotype DX Recurrence Score (RS) (low (L) 0–15, intermediate (I)16–25, high (H) ≥26) and menopausal status, with age (< 50 vs ≥50 years) used as a proxy. Overlap propensity score weighting (OPSW) balanced confounding factors (race, ethnicity, T and N stage, grade (G), and use of radiation and hormone therapies), and OPSW Cox proportional hazard models assessed the association between ILC vs IDC and OS. Results: Of the total 954,934 pts, 15% had ILC. ILC cohort had more G2 tumors (63.5 vs 49.1%), postmenopausal pts (88.4 vs 84.5%), higher clinical T stage (T2: 25.3 vs 18.7%, T3: 5.7 vs 1.2%), and node-positive disease (N1-3: 6.9 vs 6.1%). RS testing was available in 38% of pts: ILC had higher rates of L (19.4 vs 19.2) and I (16.3 vs 13.0) RS, whereas IDC had more H RS (6.0 vs 2.9), all p < 0.001. Among pts who received CT (n = 196,829), 14.2% had ILC. In this cohort, ILC pts had more G1 (23.1 vs 14.2%) and G2 (68.2 vs 47.7%) tumors, higher T stage (T2: 39.4 vs 36.3%; T3: 14.9 vs 3.0%), and nodal involvement (N1-3: 21.9 vs 18.0%) and more were postmenopausal pts (78.2 vs 69.5%). ILC pts who received CT had higher rates of L (4.3 vs 2.9), I (11.1 vs 10.3), and unknown (75.4 vs 65.6) RS, while IDC pts had higher rates of H RS (21.2 vs 9.2), all p < 0.001. ILC pts who received CT had higher mortality compared to IDC (aHR 1.20, 1.16–1.25). Univariate 10-year OS was higher for ILC (Table 1). However, ILC pts with I (aHR 1.23, 1.06–1.43) and unknown RS (aHR 1.22, 1.17–1.27) had higher mortality compared to IDC. No OS difference was observed in low (p = 0.76) or high (p = 0.92) RS groups. Among postmenopausal pts, OS differences were observed in I (aHR 1.21, 1.03–1.42) and unknown RS (aHR 1.22, 1.17–1.27). Among premenopausal pts, the difference was observed only in unknown RS (aHR 1.28, 1.15–1.43). Conclusions: We observed a discordance between clinical and genomic risk in ILC, with ILC pts presenting with higher clinical risk but low or unknown RS. CT decisions appeared driven more by clinicopathologic factors than RS suggesting that existing recurrence prediction models may not reliably predict outcomes in ILC, highlighting the need for new individualized treatment strategies. 10-year OS of ILC vs IDC pts who received adjuvant CT. RS groups ILC OS (%) IDC OS (%) p-value Overall cohort 76.2 81.8 <0.01 L 88.3 88.8 0.76 I 87.0 89.3 0.01 H 80.8 80.6 0.92

4526 Background: Despite available tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs), reliable biomarkers guiding frontline advanced RCC treatment remain limited. Existing signatures lack generalizability across therapeutic regimens. We developed a data-driven machine learning (ML) framework to predict survival outcomes and therapeutic response. Methods: Transcriptomic and clinical data were analyzed from 733 patients across two frontline treatment cohorts, sunitinib (n = 376) and avelumab plus axitinib (n = 357), derived from JAVELIN Renal 101. A multi-algorithm feature selection framework was applied to identify transcriptomic signatures associated with progression-free survival (PFS) and overall survival (OS). Prognostic performance was evaluated using the concordance index (C-index). Predictive models for therapeutic response, including disease control, were developed using PFS-derived gene signatures and assessed by area under the curve (AUC). External validation was performed in an independent cohort from The Ohio State University Total Cancer Care (OSU TCC) (n = 114). Results: ML-derived transcriptomic models consistently stratified patients into distinct risk groups with improved prognostic discrimination compared with standard clinical classifiers. In the sunitinib cohort, the best-performing models achieved C-indices of 0.72 for PFS and 0.81 for OS, outperforming IMDC (0.59 and 0.66). In the validation set of the sunitinib cohort, high-risk patients exhibited worse outcomes, with hazard ratios of 3.00 for PFS (P < 0.001, 95% CI, 2.06–4.39) and 13.42 for OS (P < 0.001, 95% CI, 7.78–23.13). In the avelumab plus axitinib cohort, C-indices reached 0.70 for PFS and 0.79 for OS. Consistent risk stratification was observed in the validation set, with hazard ratios of 3.16 for PFS (P < 0.001, 95% CI, 2.07–4.83) and 4.69 for OS (P < 0.001, 95% CI, 2.65–8.30). For response prediction, the models demonstrated predictive performance, with the Naive Bayes model achieving a validation AUC of 0.83 for disease control in both sunitinib and avelumab plus axitinib cohorts. The model showed significant risk stratification in an external validation cohort (OSU TCC). Conclusions: This study presents a multi-cohort transcriptomic framework with prognostic and predictive utility in advanced RCC. By outperforming established clinical risk classifiers and enabling prediction of regimen-specific therapeutic responses, this ML-based approach supports biomarker-informed frontline treatment selection.

BACKGROUND:The application of genomic assays, such as Oncotype DX, in patients with small (T1mi/a/b) clinically node-negative (N0-N1mi) hormone receptor positive breast cancer (HR+BC) is becoming increasingly popular in real-world clinical practice. Whether these results can be reliably prognostic among this clinically low-risk population and can be used to make decisions on the application of adjuvant chemotherapy remains unknown. PATIENTS AND METHODS:Data from the National Cancer Database (NCDB) were examined for those diagnosed with small, node-negative HR+BC between 2010 and 2020. Oncotype DX recurrence score (RS) data were classified as low (RS < 11), intermediate (RS = 11-25), or high risk (RS = 26-100). Our primary objective was comparing overall survival (OS) based on chemotherapy receipt for those with high-risk scores. Kaplan-Meier methods and multivariable Cox proportional hazard models were used. Propensity score matching was additionally performed. RESULTS:Of the 350 911 patients with T1mi/a/b N0/N0(i+)/N1mi HR+BC, 102 357 (29.2%) underwent Oncotype DX testing and had available RS data. Among them, 32 158 (31.4%) had low, 58 891 (57.5%) had intermediate, and 11 308 (11.0%) had high-risk. Chemotherapy receipt was documented in 1.2% (n = 402) of low-risk, 6.8% (n = 4000) of intermediate-risk, and 66.3% (n = 7496) of high-risk patients. Among high-risk patients, those who received chemotherapy had a lower risk of death compared with those in the chemotherapy omission arm, both on univariate (HR, 0.55; 95% CI, 0.52-0.72; P < 0.001) and multivariable (HR, 0.61; 95% CI, 0.52-0.72; P < 0.001) analyses. This finding remained true following propensity score matching (univariate HR, 0.63; 95% CI, 0.52-0.76; aHR, 0.71; 95% CI, 0.59-0.86; P < 0.001 for both). Factors associated with a high RS included higher tumor grade, Black race, and lymphovascular invasion. CONCLUSION:Our findings suggest that risk stratification through a genomic assay such as Oncotype DX may be advantageous, even among otherwise clinically low-risk individuals with HR + BC.

Trastuzumab-deruxtecan (T-DXd) is an antibody–drug conjugate (ADC) that revolutionized the treatment approach for breast cancer. However, the infectious risk associated with T-DXd is unknown. Here, we evaluate the infectious risk of T-DXd against trastuzumab-emtansine (T-DM1), an ADC with an identical monoclonal antibody. We conducted a retrospective study of consecutive breast cancer patients who received T-DXd or T-DM1. Demographic data, infection risk factors, infection sites, and opportunistic infections were recorded and compared across treatment groups. Multivariable logistic regression was used to evaluate the association between treatment group and infection, adjusting for clinical risk factors. 374 patients received T-DXd or T-DM1, with 126 receiving T-DXd alone, 196 receiving T-DM1 alone, and 52 patients receiving both treatments. Patients who received T-DXd did so as higher line of therapy (p < 0.001), was given more in the palliative setting (100

Abstract Background: Little is known regarding mixed ductal/lobular histology subtypes of breast cancer in terms of how they influence the likelihood of pathologic complete response (pCR) following neoadjuvant chemotherapy (NAC) & chemoimmunotherapy (NACI). Similarly, there is no literature on how estrogen receptor (ER) expression percentiles influence the likelihood of pCR after NACI among this mixed histology subgroup. Methods: We examined data within the National Cancer Database on those diagnosed with HER2-negative breast cancer between 2018-2022. We categorized patients based on histology subtype (ductal, lobular, & mixed) and ER percentiles, including ER-low (1-10%). Binary logistic regression was used to examine the relationship between pCR & histology type among patients with ER-low disease, adjusting for age, race, ethnicity, tumor grade, tumor size, nodal status, progesterone receptor percentiles, immunotherapy use, endocrine therapy use, & lymphovascular invasion (LVI). Additionally, adjusted Cox proportional hazards regression was fitted between overall survival (OS) and histology subtype. Results: A total of 58072 ductal, 1330 mixed, & 3500 lobular cases with available residual disease data following neoadjuvant therapy were examined. Among those with ER-low expression (ductal: 3319, mixed: 35, lobular: 80), it was shown that 46.7% (n=1549), 31.4% (n=11), & 12.5% (n=10) of patients, respectively, achieved pCR, which was a significant difference on univariate analysis (p<0.001). On adjusted analysis, the difference in pCR between patients with mixed & ductal ER-low disease was non-significant (aOR 0.69, 95%CI 0.27-1.77, reference: ductal). However, lobular patients were associated with 79% lower odds of achieving pCR compared to ductal patients (aOR 0.21, 95%CI 0.08-0.54). On multivariate analysis, variables that significantly improved the odds of achieving pCR were histology type (p=0.004), younger age (p=0.010), smaller tumor size (p<0.001), use of immunotherapy (p<0.001), and absence of LVI (p<0.001). For OS, larger tumor size (p<0.001), positive nodal status (p<0.001), & presence of LVI (p<0.001) were associated with worsened OS whereas the receipt of endocrine therapy (p=0.003) significantly improved OS. Histology subtype was not significantly associated with differences in OS (p=0.238). Conclusion: Tumors of mixed ductal/lobular histology behave more similarly to ductal disease in terms of NACI response, including ER-low subgroups. Additional factors, such as tumor size, LVI, and receipt of immunotherapy play a stronger role in predicting pCR, but the histology subtype remains an important variable to consider when deciding how best to sequence treatment for those with HER2-negative ER-expressing disease. Citation Format: Kai C. Johnson, Brandon Slover, Yengeniya Gokun, Dionisia M. Quiroga, Sagar Sardesai, Sachin Jhawar, Nerea Lopetegui-Lia, Arya M. Roy, Gilbert Bader, Ashley P. Davenport, Nicole O. Williams, Robert Wesolowski, Margaret E. Gatti-Mays, Daniel G. Stover, . Impact of histology subtypes (ductal, lobular, and mixed ductal/lobular) on pathological complete response (pCR) following neoadjuvant chemotherapy (NAC) and chemoimmunotherapy (NACI) for estrogen receptor low (ER-low) HER2-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6646.

Introduction Bone metastasis in breast cancer patients can lead to poor quality of life and cause significant morbidity. First-line treatments for bone metastases include bone modifying agents that inhibit osteoclast activity to reduce bone resorption. The most commonly used bone modifying agents include zoledronic acid and denosumab. It is currently unknown whether a therapy change from zoledronic acid to denosumab would result in greater symptom control or improved disease outcomes in breast cancer patients. In this single institution retrospective cohort study, we aimed to identify the reasons why patients transitioned from zoledronic acid to denosumab and to determine whether denosumab, as a second-line therapy, led to reduced narcotic use for metastatic bone pain. Materials and Methods Breast cancer patients with bone metastases treated at The Ohio State University Comprehensive Cancer center from 2011-2018 with a bone modifying agent were examined. Nineteen patients who received second-line denosumab following previous exposure to zoledronic acid were identified and included in this study. Results The two most common reasons patients switched from zoledronic acid to denosumab were related to side effect burden (28%) and bone metastasis progression (28%). No reduction in narcotic use due to bone pain was observed six months after starting denosumab. None of the patients developed new skeletal related events after switching to Denosumab. Conclusion Overall, denosumab as a second-line therapy appeared to be well-tolerated in our cohort, as we did not observe any treatment-related toxicity. Patients who switched to denosumab did not experience improved pain control.

e15041 Background: Decreased baseline and cycle 1 (C1) serum albumin (ALB) predicts shorter overall survival (OS) in pts with NSCLC treated with IgG based immune checkpoint inhibitors (ICIs) but not in pts on ICI + chemotherapy. Decreased ALB may serve as a marker of hyper catabolism associated with cancer cachexia and elevated clearance (CL) of IgG drugs. It is unknown if ALB associates with OS in pts treated with antibody drug conjugates (ADCs), which combine IgG and chemotherapy. In this single-center retrospective analysis, we aimed to assess ALB and OS association. Methods: 601 cancer pts who received ADC, with at least one pretreatment and one on-treatment (C1) ALB value were included. Normal ALB was ≥ 3.5 g/dL per institution cut-off. In a subset of pts with pretreatment ALB measured within 30 days prior to start of ADC (n = 503), ALB percent change was calculated relative to the C1 value. ALB change was categorized as Increase/No change/Decrease and ( > 0%, 0% to –5%, –5% to –10%, < –10%). OS was defined as time from first ADC dose to death/censoring at most recent contact. Cox proportional hazards regression assessed ALB percent change vs. OS, adjusted for ADC and cancer type. Effect modification by ADC and cancer was evaluated with interaction terms (e.g. ADC x ALB change). Kaplan-Meier plots were used to visualize differences in OS. Results: In the full dataset (n = 601), pretreatment and C1 ALB were strongly associated with OS. Pts with normal (n = 490) vs. low ( < 3.5 g/dL, n = 111) C1 ALB had median OS of 31 mo vs. 9.8 mo, respectively (p < 0.0001). Among pts with relevant pre-treatment ALB (n = 503), pts with ALB increase had median OS of 29.3 mo, while pts with 0 – 5%, 5-10% or ≥10% decrease had median OS of 23.4 mo, 17.7 mo, and 12.1 mo, respectively ( p < 0.001). In multivariable analysis, each 10% ALB increase associated with 22% reduction in hazard of death (HR = 0.78, 95% CI 0.67–0.92). Compared with pts experiencing ≥10% ALB decrease, those with smaller decrease or increase had significantly lower hazards (adjusted HRs 0.45–0.49, all p < 0.001); highlighting the strong association of large ALB drop on survival. Borderline and significant interactions were observed comparing ALB (% change) vs cancer type (p = 0.06) and ALB change (3 categories) vs ADC type (p = 0.046), respectively. Conclusions: Early ALB change was independently associated with OS in cancer pts receiving ADCs, suggesting it may serve as an early indicator of mechanisms underlying IgG-based therapy resistance. Given known link between ALB and CL of IgG-based drugs, further investigation into the CL patterns, toxicities and outcomes of pts treated with ADCs is warranted. Age; Male(n), Female (n) 58 (24–89); 91, 510 Breast cancer, n (%) 402 (67) Non-breast, n (%) 199 (33) Advanced stage (3 - 4), n (%) 231 (38) Stage ≤ 2, n (%) 284 (47) Stage unknown, n (%) 86 (14) Trastuzumab Emtansine, n (%) 210 (35) Trastuzumab Deruxtecan, n (%) 147 (25) Enfortumab Vedotin, n (%) 91 (15) Other ADC, n (%) 153 (26)

Background Exploitation of the sodium iodide symporter (NIS) has potentially broad clinical application across different tumour ablative settings but often fails in aggressive cancer due to diminished transport activity. We aimed to discover whether enhancing NIS function by modulating proteostasis was targetable in vivo, as well as the clinical relevance to radioiodide (RAI) treatment of patients with cancer. Methods We used 3D modelling, iterative design, reformulation, RAI uptake, RNA-Seq, cell surface biotinylation assays and NanoBRET in transformed cell lines and primary thyroid cells from patients to identify new drugs targeted at enhancing NIS function and to uncover their respective mechanisms. Systemic drug responses were monitored via 99mTc pertechnetate gamma counting and SPECT/CT imaging in wild-type BALB/c and Tg-rtTA/tetO-BRAFV600E mice, as well as orthotopic NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) breast cancer. Findings Copper diethyldithiocarbamate (Cu(DDC)2), a metabolite of the FDA-approved drug disulfiram, modulated NIS function in thyroid and breast cancer cells (P < 0.05). Mechanistically, Cu(DDC)2 elicited a dual effect on NIS function, targeting valosin containing protein (VCP)—a key regulator of proteostasis—as well as inducing potent transcriptional responses (P < 0.05). In mice, the copper-bound metabolite stimulated NIS activity in normal thyroid tissue, thyroid tumours and in breast orthotopic tumours (P < 0.05), the latter augmented by the histone deacetylase inhibitor vorinostat (SAHA). Notably, there was clinical association of drug-perturbed genes in RAI-treated thyroid cancer, enabling construction of a robust dual risk score classifier for predicting recurrence (AUC >0.95; P < 0.001). Interpretation Our findings reveal a mechanistic pathway towards enhancing radionuclide uptake in vivo, with clinical relevance for RAI therapy and identifying survival indicators of recurrent disease. Funding This work was funded by the U.S. Department of Defense (BC201532P1), Medical Research Council (CiC/1001505 and MR/Z504828/1), British Thyroid Foundation (1002175). We further acknowledge support from the Wellcome Trust and EPSRC funded Centre for Medical Engineering at King’s College London (203148/Z/16/Z), the Wellcome Multiuser Equipment Radioanalytical Facility (212885/Z/18/Z), and the EPSRC programme for Next Generation Molecular Imaging and Therapy with Radionuclides (EP/S019901/1).

Background: Genomic alterations guide therapeutic decisions in solid tumours, but next-generation sequencing (NGS) is inaccessible in a substantial fraction of clinical settings, whereas haematoxylin and eosin (H&E)-stained slides are universally available. We aimed to determine whether actionable genomic alterations and patient prognosis can be inferred directly from routine histopathology across cancer types. Methods: We developed GenoGlyph, an interpretable deep-learning framework that models histopathology at cellular and tissue scales, spatially biased cell attention, and outer-product cross-scale fusion. Using 6391 whole-slide images spanning 14 solid tumour types from The Cancer Genome Atlas, we trained cancer-type-specific models for 34 mutation-prediction tasks across 14 genes and organ-agnostic pan-cancer models for TP53, KRAS, APC, PIK3CA, KMT2D, and TTN. We assessed transcriptomic concordance with matched RNA-seq using single-sample gene-set enrichment analysis, and evaluated the prognostic value of the learned representations in four independent external cohorts (n=982). Findings: GenoGlyph predicted somatic mutations with areas under the curve of 0·63–0·98, exceeding previous pan-cancer benchmarks. Organ-agnostic models frequently matched or surpassed cancer-type-specific models. Morphologically inferred mutation states, including sequencing-discordant cases, showed transcriptional pathway activity concordant with the inferred genotype. The learned representations independently stratified overall, disease-free, recurrence-free, and progression-free survival across cohorts. Interpretation: Genotype–phenotype relationships are systematically legible from standard H&E sections. By treating mutation prediction as a supervision signal rather than an endpoint, GenoGlyph offers a scalable, biologically grounded approach to genomic inference, functional variant interpretation, and survival stratification where molecular testing is limited.

Neoadjuvant chemotherapy (NAC) has become an integral component of modern breast cancer management, particularly for patients with triple-negative and HER2-positive disease. As pathologic complete response (pCR) rates improve with current systemic therapies, the optimal role of adjuvant radiotherapy (RT) after NAC has become an area of active investigation. This review summarizes current evidence, evolving guidelines, and ongoing clinical trials evaluating RT de-escalation and treatment personalization after NAC. Response to NAC is strongly associated with clinical outcomes and increasingly informs locoregional treatment decisions. The phase III NSABP B-51/RTOG 1304 trial demonstrated low recurrence rates and no significant benefit at 5 years from regional nodal irradiation (RNI) in patients with cT1-3N1 disease who achieved ypN0 status after NAC, findings now incorporated into updated NCCN and ASTRO-ASCO-SSO guideline recommendations. Additional studies, including RAPCHEM and retrospective analyses, support selective de-escalation of RNI in carefully selected patients, while ongoing trials continue to evaluate omission of whole-breast irradiation (WBI) and optimization of axillary management. Emerging data also highlight the increasing complexity of integrating RT with modern adjuvant systemic therapies, including immunotherapy, HER2-targeted agents, CDK4/6 inhibitors, and PARP inhibitors. Advances in systemic therapy and improved response assessment are driving a transition toward more individualized, response-adapted RT strategies after NAC. Ongoing trials, including Alliance A011202, ADARNAT, ATNEC, DESCARTES, and ROSALIE, will further define the optimal integration, sequencing, and de-escalation of RT in the post-NAC setting.