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Abstract BackgroundFormalin-fixed, paraffin-embedded (FFPE) tissues have many advantages for identification of risk biomarkers, including wide availability and potential for extended follow-up endpoints. However, RNA derived from archival FFPE samples has limited quality. Here we identified parameters that determine which FFPE samples have the potential for successful RNA extraction, library preparation, and generation of usable RNAseq data.MethodsWe optimized library preparation protocols designed for use with FFPE samples using seven FFPE and Fresh Frozen replicate pairs, and tested optimized protocols using a study set of 130 FFPE biopsies from women with benign breast disease. Metrics from RNA extraction and preparation procedures were collected and compared with bioinformatics sequencing summary statistics. Finally, a decision tree model was built to learn the relationship between pre-sequencing lab metrics and qc pass/fail status as determined by bioinformatics metrics.ResultsSamples that failed bioinformatics qc tended to have low median sample-wise correlation within the cohort (Spearman correlation < 0.75), low number of reads mapped to gene regions (< 25 million), or low number of detectable genes (11,400 # of detected genes with CPM > 2). The median RNA concentration and pre-capture library Qubit values for qc failed samples were 18.7 ng/ul and 1.68 ng/ul respectively, which were significantly lower than those of qc pass samples (40.8 ng/ul and 5.58 ng/ul). We built a decision tree model based on input RNA concentration, input library qubit values, and achieved an F score of 0.803 in predicting QC status (pass/fail) of FFPE samples. ConclusionsWe provide a bioinformatics quality control recommendation for FFPE samples from breast tissue by evaluating bioinformatic and sample metrics. Our results suggest a minimum concentration of 25 ng/ul FFPE-extracted RNA for library preparation and 1.8 ng/ul pre-capture library output to achieve adequate RNA-seq data for downstream bioinformatics analysis.

Abstract Background:Breast terminal duct lobular units (TDLUs), the source of most breast cancer (BC) precursors, are shaped by age-related involution, a gradual process, and postpartum involution (PPI), a dramatic inflammatory process that restores baseline microanatomy after weaning. Dysregulated PPI is implicated in the pathogenesis of postpartum BCs. We propose that assessment of TDLUs in the postpartum period may have value in risk estimation, but characteristics of these tissues in relation to epidemiological factors are incompletely described.Methods: Using validated Artificial Intelligence and morphometric methods, we analyzed digitized images of tissue sections of normal breast tissues stained with hematoxylin and eosin from donors <45 years from the Komen Tissue Bank (180 parous and 545 nulliparous). Metrics assessed by AI, included: TDLU count; adipose tissue fraction; mean acini count/TDLU; mean dilated acini; mean average acini area; mean “capillary” area; mean epithelial area; mean ratio of epithelial area versus intralobular stroma; mean mononuclear cell count (surrogate of immune cells); mean fat area proximate to TDLUs and TDLU area. We compared epidemiologic characteristics collected via questionnaire by parity status and race, using a Wilcoxon rank sum test or Fisher’s exact test. Histologic features were compared between nulliparous and parous women (overall and by time between last birth and donation [ recent birth: ≤5 years versus remote birth: >5 years]) using multivariable regression models.Results:Normal breast tissues of parous women contained significantly higher TDLU counts and acini counts, more frequent dilated acini, higher mononuclear cell counts in TDLUs and smaller acini area per TDLU than nulliparas (all multivariable analyses p<0.001). Differences in TDLU counts and average acini size persisted for >5 years postpartum whereas increases in immune cells were most marked <5 years of a birth. Relationships were suggestively modified by several other factors, including demographic and reproductive characteristics, ethanol consumption and breastfeeding duration. Conclusions:Our study identified sustained expansion of TDLU numbers and reduced average acini area among parous versus nulliparous women and notable increases in immune responses within five years following childbirth. Further, we show that quantitative characteristics of normal breast samples vary with demographic features and BC risk factors.

580 Background: Patients (pts) in Cohort A of the American College of Surgeons Oncology Group Z1031 (Alliance) trial of NET for cStage II-III breast cancer were randomized to anastrozole [ANA], letrozole [LET] or exemestane [EXE] for 16-18 weeks (wks). In Cohort B, pts chose between ANA and LET and switched to chemotherapy or surgery if a tumor biopsy after 2-4 wks of NET had Ki67 >10%. Treatments after surgery were not defined by the trial protocol. SET2,3 measures nonproliferation gene expression related to estrogen and progesterone receptors adjusting for a baseline prognostic index that combines clinical tumor and nodal stage and a 4-gene molecular subtype (RNA4) defined by ESR1, PGR, ERBB2 and AURKA. High SET2,3 in a pre-treatment biopsy using cStage information is defined as SET2,3 >1.77. Methods: 379 pts had gene expression data from a research tumor biopsy prior to NET (Agilent 44K microarrays). A bioinformatician blinded to pt treatment and clinical outcomes determined SET2,3. The trial statistician then examined the association between SET2,3 and pharmacodynamic response at 2-4 wks (N=141, Cohort B): Ki67 ≤10% and complete cell cycle arrest (CCCA Ki67 ≤2.7%); pathologic outcomes in pts who completed NET: ypStage 0/1 (N=329, Cohorts A&B), PEPI-0 rate (N=155, Cohort B); and event-free survival (EFS) post-registration (N=244, Cohorts A&B). We used Fisher’s exact tests to assess whether responses, and Cox modeling to evaluate whether EFS, differed with respect to SET2,3 status. Results: High SET2,3 in 48% of pts (183/379) was associated with older age (median: 66 vs 63 years; p=0.012); cStage II (95% vs 75%; p <0.001); and pre-NET Ki67 ≤10% (37% vs 20%; p< 0.001) in pts with low SET2,3. In Cohort B, pts with high SET2,3 had a higher rate of pharmacodynamic response in their tumor at wk 2-4 than pts with low SET2,3 (Table). In the subset of Cohort B pts with wk 2-4 Ki67 ≤10%, pre-treatment SET2,3 trended numerically higher in pts who achieved PEPI-0 score (p=0.049) but the proportion achieving PEPI-0 did not differ by SET2,3 high/low status (Table). EFS was significantly longer for pts with high SET2,3 than pts with low SET2,3 (HR[H/L]: 0.52; 95% CI: 0.34-0.80; p=0.003). Conclusions: An exploratory analysis of Z1031 data demonstrated that the rate of pharmacodynamic suppression of proliferation by NET at 2-4 wks was greater and EFS was longer for pts with breast cancer expressing high SET2,3 disease than pts with low SET2,3. Support: U10CA180821, U10CA180882, U24CA196171; https://acknowledgments.alliancefound.org ; Clinical trial information: NCT00265759. [Table: see text]

Abstract Background: Women with benign breast disease (BBD) are at increased risk of breast cancer (BC). Classic studies based on film-based mammographic screening and pathology diagnosis of surgical biopsies conducted in the 1980s established a hierarchy of increasing BC risk: non-proliferative (NP) BBD, proliferative BBD without atypia (PDWA) and atypical hyperplasia (AH). Given changes in epidemiological BC risk factors and introduction of percutaneous core needle biopsy (CNB) in mid-1990s, and later, digital mammography, we hypothesized that the patient characteristics and relative frequency of BBD diagnoses have changed over time. Accordingly, we performed a longitudinal analysis of the frequency of patient characteristics and BBD diagnoses in the Mayo BBD cohort. Methods: Utilizing the Mayo Clinic Surgical and Pathology Indices, women ages 18 to 85 who had a BBD biopsy between 1/1/67 and 12/31/13 were identified. Breast pathologists reviewed biopsies masked to diagnoses of incident BC diagnosed in follow-up. Demographic characteristics and BC events were obtained by query of institutional data sources and participant surveys. Trends were evaluated for the following eras: 1: pre-mammogram (1967-1981), 2: pre-CNB (1982-1992), 3: CNB Transition (1993-2001), and 4: CNB (2002-2013). Demographics were formally compared across eras using chi-square tests for categorical variables and analyses of variance (ANOVAs) for continuous variables. Results: From 1967-2013, the cohort includes 19,582 unique women with BBD. The frequency of CNB increased from eras 1-4: 0.04%, 0.6%, 51.3 %, and 88.9%, respectively. Mean age at BBD diagnosis was younger in era 1 (48.0 years) vs eras 2-4 (53.2, 52.0, and 51.8, respectively, p&lt;0.001). The percentage of biopsies diagnosed as PDWA increased from era 1-4 (25.7%, 34.3%, 35.2%, 46.2%, p&lt;0.001), as did the percentage with AH (2.4%, 5.1%, 8.6%, 12.3%, p&lt;0.001). Over eras 1-4, the percentages of women with a strong family history of BC increased (9.9%, 12.7%, 17.1%, and 29.0%, p&lt;0.001) as did mean BMI (24.8, 26.4, 27.4, and 28.6, p&lt;0.001). With a median follow-up of 10.9 years, 1,719 breast cancers have developed, with increasing proportion of noninvasive (DCIS-only) disease across eras 1-4: 15.0%, 21.1%, 21.2%, and 33.2%, p&lt; 0.001. Conclusions: Analysis of this large, single institution BBD cohort for the 46 year period 1967-2013 demonstrates that BC risk factors among BBD patients has changed over time, with subjects demonstrating increasing age, BMI, and family history, and that the percentages of BBD classified as PDWA and AH have increased. Impact on BC risk will be further investigated. Citation Format: Amy Degnim, Karthik Ghosh, Jodi M Carter, Robert A Vierkant, Matthew R Jensen, Stacey J Winham, Tanya L Hoskin, Marlene Frost, Teresa M Allers, Denise L Gehling, Mindy J Kern, Laura M Pacheco-Spann, Celine M Vachon, Derek C Radisky, Daniel W Visscher, Mark E Sherman. Benign breast disease: Temporal trends from 1967 to 2013 [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS7-11.

Abstract Background: Women with benign breast disease (BBD) experience an increased risk of breast cancer (BC). Histologic classification of BBD, as non-proliferative disease (NP), proliferative disease without atypia (PDWA) or atypical hyperplasia (AH), stratifies groups of patients into progressively higher categories of BC risk. However, this classification does not comprehensively assess the proliferative state of the epithelium throughout the biopsy. In addition, while AH is considered the most high-risk class of BBD, it is not always a highly proliferative lesion; atypical ductal lesions may reflect focal cytologic and architectural changes. We evaluated the association of an alternative classification of BBD severity and BC risk based on subjective grading of: 1) the maximal degree of epithelial proliferation and 2) multifocality of epithelial proliferation. Methods: Pathologists reviewed biopsies from participants aged 18 to 85 years in the Mayo BBD cohort (2002-2013), masked to BC outcomes, ascertained via questionnaires, tumor registry data and medical record review. Biopsies were classified as NP, PDWA or AH and semi-quantitatively scored for: 1) maximal degree of epithelial proliferation within a focus (DP) (0-3; none to severe) and 2) multifocality of proliferation (MP) (0-3; none to multiple foci). DP and MP scores were also summed to give a DP+MP score (0-6). Associations of DP and MP with BC risk were examined using Cox proportional hazards regression analyses, adjusting for age at BBD biopsy. Women were followed from date of initial biopsy to date of BC, death or last follow-up. Results: Of the 1529 assessable biopsies, 544 (35.6%) were classified as NP, 708 (46.3%) as PDWA and 277 (18.1%) as AH. Both DP and MP scores had significant positive correlation with increasing BBD severity (DP: r=0.51, p&lt; 0.001; MP: r=0.52, p&lt; 0.001). Mean (SD) DP scores were 0.6 (0.6) for NP, 1.6 (0.9) for PDWA, and 1.8 (0.7) for AH (ANOVA p&lt;0.001). Mean (SD) for MP scores were 0.6 (0.6) for NP, 1.4 (0.8) for PDWA, and 1.8 (0.8) for AH (ANOVA p&lt;0.001). Mean (SD) for DP+MP scores were 1.2 (1.2) for NP, 2.9 (1.5) for PDWA, and 3.6 (1.2) for AH (ANOVA p&lt;0.001). With median follow-up of 8.8 years for controls and 5.3 years for cases, 10.6% of the women in the cohort developed BC. Compared to those with DP scores of 0, women with DP scores of 3 had significantly increased BC risk (HR 1.42, 95% CI: 1.16, 1.74, p=0.003). MP was associated with a non-significant increase in BC risk for scores of 3 versus 0 (HR: 1.20, 95% CI: 0.97,1.49, p=0.11). DP+MP scores of 6 conferred the highest BC risk (HR (score 6 vs. 0): 1.62, 95% CI 1.18,2.21, p=0.02). Results did not substantively differ after adjusting for BBD severity as NP, PDWA or AH. Conclusions: In this preliminary analysis within the Mayo BBD cohort, both proliferative degree (DP) and multifocality (MP) scores were correlated with histologic severity of BBD. DP and DP+MP scores were each associated with increased BC risk. We conclude that improved characterization of epithelial proliferation in BBD biopsies may enable refined prediction of individual BC risk. Citation Format: Jodi M Carter, Matthew R Jensen, Robert A Vierkant, Stacey J Winham, Tanya L Hoskin, Marlene Frost, Karthik Ghosh, Derek C Radisky, Amy C Degnim, Mark E Sherman. Epithelial proliferation score as an independent breast cancer risk predictor in benign breast disease [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD10-10.

Abstract Background: About 10-20% of breast cancers (BC) are hereditary. Patients (pts) with BC and pathogenic variants (PVs) in the BRCA1 or BRCA2 gene are at increased risk of contralateral breast cancer (CBC). However, risks of CBC and recurrent BC associated with PVs in other established BC predisposition genes (e.g. PALB2, CHEK2, ATM) are less well established. We performed research genetic testing of over 4000 pts with BC treated at Mayo Clinic. Because most pts were unaware of their mutation status prior to selection of therapy, this cohort provides a unique opportunity to evaluate the natural history of BC recurrence after treatment of the index BC in pts with a deleterious mutation in predisposition genes. Methods: Women diagnosed and treated for BC at Mayo Clinic between 2001-2016 were enrolled in an IRB-approved research cohort. Pts were not selected for age of diagnosis or family history. Coding regions and consensus splice sites of established BC predisposition genes were subjected to amplicon-based deep sequencing using the QIAseq method in germline DNA extracted from peripheral blood or buccal samples. PVs in the genes were identified using GATK haplotype caller and Vardict. Patient, tumor, oncologic management and follow up data were reviewed. Herein we report on the differences in the incidence of local recurrence and CBC, among carriers and non- carriers and by predisposition gene using the cumulative incidence estimator and Fine and Gray regression to account for competing risks. Results: Of 4272 women with BC (median age 57, range 21-94), a PV in a BC predisposition gene was identified in 287 (6.7%). The most common PVs were seen in CHEK2 (67, 1.6%), BRCA1 (62, 1.5%), BRCA2 (60, 1.4%), ATM (47, 1.1%); 8 (0.2%) patients had more than one PV. Pts with any PVs presented at younger age (median 51 vs 58 years, p&lt;0.001), were more likely to have stage IV disease (4.5% vs 2.3%, p=0.02), estrogen receptor (ER) negative BC (19.5% vs 13.5%, p=0.005), and bilateral cancer (8.8% vs 4.3%, p&lt;0.001). ER- BC proportion varied by gene - 38.9% in pts with PVs associated with ER- BC (BRCA1, BARD1, BRIP1, RAD51C, RAD51D) and 11.9% in those with PVs associated with ER+ BC (BRCA2, ATM, CDH1, CHEK2), p&lt;0.001. NCCN criteria for genetic testing were assessed for 3763, with 69.9% of those with PVs qualifying for testing compared to 46.3% of those without PVs (p&lt;0.001). Among 3973 pts (249 with PVs, 3724 without) with unilateral stage 0-III cancer, 168 pts developed local recurrence, 131 pts developed CBC and 248 pts developed distant disease with median 5 years of follow-up. Patients with any PV had higher rates of local recurrence after breast conserving surgery (HR 2.3, 95% CI: 1.2-4.2, p=0.01) but overall did not have significantly higher risk of CBC if contralateral prophylactic mastectomy was not performed (HR 1.7, 95% CI: 0.9-3.2, p=0.11). Specifically, pts with PVs in genes other than BRCA1 or BRCA2 did not have a higher risk of CBC compared to pts with no PV (HR 1.0, 95% CI: 0.4-2.7, p=0.96); the comparison of CBC risk in BRCA1 or BRCA2 versus those with no PV was HR 2.9 (95% CI: 1.3-6.5, p=0.01), while BRCA1 or BRCA2 compared to those with other PVs was HR 2.7 (95% CI: 0.8-9.3, p=0.13). In pts with CHEK2 PVs, the probability of CBC at 10-years follow-up was 5.4%, which was similar to pts without a mutation (5.3%), p=0.47. Conclusion: While PVs in known BC predisposition genes increase risk of BC development, risk of CBC in carriers of PVs in non-BRCA predisposition genes with BC are not as high as seen with BRCA1 or 2 PV carriers. This information regarding future BC will be helpful in personalizing decisions on the extent of surgery in carriers of PVs in BC predisposition genes. Citation Format: Judy C. Boughey, Tanya L. Hoskin, Siddhartha Yadav, Fergus J. Couch. The influence of pathogenic variants in breast cancer predisposition genes on secondary breast cancer events in a prospectively collected cohort of breast cancer patients [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD10-02.

Abstract Background: Achieving a pathologic complete response (pCR) has been shown on the patient level to predict excellent long-term event-free survival outcomes. Residual cancer burden (RCB) quantifies the extent of residual disease for patients who did not achieve pCR. We have previously observed in the I-SPY 2 TRIAL that while metastatic events outside the central nervous system (CNS) were dramatically reduced in the setting of pCR, the incidence of CNS metastasis remained similar across RCB classes, raising the possibility that these CNS events may be independent of response in the breast. In this study, we evaluate the type and sites of recurrences by RCB in a large pooled dataset, which allows for analysis within subtype, to validate these findings. Methods: 5161 patients pooled across 12 institutions/trials with available RCB and event-free survival (EFS) data were included in this analysis. EFS was calculated as the interval between treatment initiation, and locoregional recurrence, distant recurrence or death from any cause; patients without event are censored at time of last follow-up. The median follow-up is 4.6 years. We summarized the EFS event type, further sub-dividing the distant recurrence events (DR) by their site of relapse (CNS-only, CNS and other sites, Non-CNS). We used a competing risk (Fine-Gray) model to assess which of these site-specific relapses differ between RCB classes and estimated the cumulative incidence of CNS-only and non-CNS events at 5 years. Analyses were performed across the entire study population and within HR/HER2 defined subtypes. Results: Among the 5161 subjects, there were 1164 EFS events, including 92 (7.9%) local recurrences (without distant recurrence and/or death) and 1072 distant recurrence-free survival (DRFS) events. Among the DRFS events, 158 patients died without a distant recurrence. 914 experienced distant recurrences, including 90 (9.8%) with CNS-only, 145 (15.9%) with CNS and other sites, 664 (72.6%) with non-CNS distant recurrence; 15 (1.6%) patients had missing recurrence site information. Table 1 summarizes the cumulative incidence of CNS-only and non-CNS recurrence at 5 years and the proportion of CNS-only recurrences among DR events by RCB class overall and within each HR/HER2 subtypes. The incidence of CNS-only recurrences was low and similar across RCB classes. In contrast, the incidence of non-CNS recurrences increases with increasing RCB. As a result, CNS-only recurrences are proportionally higher within the RCB-0 and RCB-I than in the RCB-II and RCB-III groups, largely because of the low DR event rate and relative low frequency of non-CNS recurrence events within the RCB-0 and RCB-I classes. Overall, 27% of the recurrences in the setting of pCR (RCB-0) are due to CNS-only recurrences.Conclusions: Consistent with previous studies, our large pooled analysis confirmed that CNS-only recurrences are uncommon but appear similar across RCB groups, independent of response, suggesting that the CNS is a treatment sanctuary site. In contrast, non-CNS recurrence rates increase as RCB increases. These findings suggest that inclusion of CNS-only recurrences as an outcome event may impact the association between neoadjuvant therapy response and long-term outcomes in the context of current therapies. Novel therapies that cross the blood brain barrier will be needed to impact CNS recurrence rates. Table 1: Cumulative Incidence of CNS Only and non-CNS Distant Recurrences at 5 years and proportion of CNS-only events among DR eventsRCB Class0IIIIIIpOverall (5161)N16766622017806Cum. Inc. CNS Only2%2%2%1%0.627Cum. Inc. Non-CNS3%6%16%27%&lt;0.001# CNS-Only / # DR events (%)26/96 (27%)14/74 (19%)39/443 (9%)11/301 (4%)HR-HER2- (1774)N770212590202Cum. Inc. CNS Only2%3%2%4%0.298Cum. Inc. Non-CNS4%11%19%42%&lt;0.001# CNS-Only / # DR events (%)13/50 (26%)6/32 (19%)13/148 (9%)8/111 (7%)HR-HER2+ (572)N3766710029Cum. Inc. CNS Only1%5%5%0%0.022Cum. Inc. Non-CNS2%5%18%38%&lt;0.001# CNS-Only / # DR events (%)4/17 (24%)3/10 (30%)6/31 (19%)0/13 (0%)HR+HER2+ (858)N31317229182Cum. Inc. CNS Only1%1%2%0%0.37Cum. Inc. Non-CNS2%3%15%26%&lt;0.001# CNS-Only / # DR events (%)3/10 (30%)2/16 (12%)7/68 (10%)0/29 (0%)HR+HER2- (1957)N2172111036493Cum. Inc. CNS Only3%2%1%0.2%0.087Cum. Inc. Non-CNS5%4%13%20%&lt;0.001# CNS-Only / # DR events (%)6/19 (32%)3/16 (19%)13/196 (7%)3/148 (2%) Citation Format: Sonal Shad, Marieke van der Noordaa, Marie Osdoit, Diane de Croze, Anne-Sophie Hamy, Marick Lae, Fabien Reyal, Miguel Martin, María Del Monte-Millán, Sara López-Tarruella, I-SPY 2 TRIAL Consortium, Judy C Boughey, Matthew P Goetz, Tanya Hoskin, Rebekah Gould, Vicente Valero, Gabe Sonke, Tessa G Steenbruggen, Maartje van Seijen, Jelle Wesseling, John Bartlett, Stephen Edge, Mi-Ok Kim, Jean Abraham, Carlos Caldas, Helena Earl, Elena Provenzano, Stephen-John Sammut, David Cameron, Ashley Graham, Peter Hall, Lorna Mackintosh, Fan Fang, Andrew K Godwin, Kelsey Schwensen, Priyanka Sharma, Angela DeMichele, Janet Dunn, Louise Hiller, Larry Hayward, Jeremy Thomas, Kimberly Cole, Lajos Pusztai, Laura Van't Veer, Fraser Symmans, Laura Esserman, Christina Yau. Site of recurrence after neoadjuvant therapy: A multi-center pooled analysis [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD13-02.

Abstract Background: Pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with breast cancer is associated with improved survival. Further assessment of the extent of residual disease, using the pathological anatomic American Joint Committee on Cancer staging method (ypStage) or the Residual Cancer Burden (RCB) method, have been shown to add prognostic information for patients with residual disease. Neo-Bioscore, an alternate system to classify response to NAC, includes clinical stage at diagnosis and biology and defines eight prognostic groups. The goal of this study was to compared three scoring systems (anatomic ypStage (7th ed), RCB Class and Neo-Bioscore) and assess whether RCB Class and Neo-Bioscore provide additional prognostic value in the context above anatomic ypStage, the most commonly used method for post-neoadjuvant residual disease assessment. Methods: Data from 5161 patients treated with NAC was pooled from 12 sites. Patients without clinical and pathological staging were excluded, as were patients with HER2+ breast cancer who did not receive neoadjuvant HER2-targeted therapy, leaving 3730 for analysis. PCR was defined as no residual invasive tumor in breast and nodes, i.e. RCB-0 or ypT0/Tis and ypN0. Patients with discordant pCR status by RCB Class vs ypStage (n=9) were excluded. Associations between each scoring system and event-free survival (EFS) were evaluated using the log rank test. EFS at 5 years was estimated using the Kaplan Meier method. Associations between Neo-Bioscore and EFS were assessed in the pCR group. For patients with residual disease, we assessed RCB and Neo-Bioscore within each ypStage. Analysis was performed overall and within subtype. Subgroups with &lt;5 patients were excluded from the survival analyses. Results: ypAJCC staging, RCB class and Neo-Bioscore were all associated with EFS in the overall population and within each subtype (log rank p&lt;0.0001). Of note, 13 patients with a Neo-Bioscore of 7 all recurred or died within 19 months of follow-up. Overall, 34% (1264/3721) of patients achieved a pCR. Their Neo-Bioscore ranges from 0-5, where 3% (37/1264) has a Neo-Bioscore of 5 despite achieving pCR. The Neo-Bioscore was not associated with EFS in case of a pCR, with EFS estimates at 5 years of 95%, 94%, 92%, 93%, 90% and 92% for Neo-Bioscores 0-5 respectively. As HR and HER2 status are components of the score, the range of Neo-Bioscore in the pCR group differs by subtype. However, similar to the overall analysis, the Neo-Bioscore was not prognostic within subtypes in case of pCR. Overall, among the patients who did not achieve pCR, both RCB class and Neo-Bioscore were associated with EFS within ypStages I, II and III. However, the ypStage within which RCB and Neo-Bioscore are prognostic is different for each subtype. RCB class was prognostic in ypStage I in both HR+ subtypes: patients with ypStage-I/RCB-I had significantly improved survival compared to patients with ypStage-I/RCB-II (5-year EFS: 100% vs 83% in HR+HER2- and 95% vs 77% in HR+HER2+). In contrast, for patients with triple negative breast cancer, RCB class was prognostic within ypStage II and III. Analysis by clinical stage and the components of the three systems that contribute most to prognosis will be presented. Conclusions: The degree of response to NAC adds important information to pCR versus residual disease. The Neo-Bioscore was not prognostic among patients with pCR, suggesting that clinical stage (including subtype and grade) adds little information in the setting of a pCR. In contrast, both RCB and Neo-Bioscore provide additional prognostic information to the conventional ypAJCC staging among non-pCR patients, suggesting that clinical stage, tumor biology as well as extent of residual disease all contribute to prognosis in the setting of residual disease after NAC. Citation Format: Marieke EM van der Noordaa, Christina Yau, Sonal Shad, Marie Osdoit, Tessa G Steenbruggen, Diane de Croze, Anne-Sophie Hamy, Marick Lae, Fabien Reyal, Maria Del Monte-Millán, Miguel Martin, Sara Lopez Tarruella, I-SPY 2 TRIAL Consortium, Judy C Boughey, Matthew Goetz, Tanya Hoskin, Rebecca Gould, Vincent Valero, Gabe Sonke, Maartje van Seijen, Jelle Wesseling, John Bartlett, Stephan Edge, Mi-Ok Kim, Jean Abraham, Carlos Caldas, Helena Earl, Elena Provenzano, Stephen-John Sammut, David Cameron, Ashley Graham, Peter Hall, Lorna MacKintosh, Fang Fan, Andrew K Godwin, Kelsey Schwensen, Priyanka Sharma, Angela DeMichele, Janet Dunn, Louise Hiller, Larry Hayward, Jeremy Thomas, Kimberley Cole, Lajos Pusztai, Laura van 't Veer, Fraser Symmans, Laura Esserman. Assessing prognosis after neoadjuvant therapy: A comparison between anatomic ypAJCC staging, residual cancer burden class and neo-bioscore [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr GS4-07.

Background: While HL is a highly curable cancer, patients (pts) with advanced stage disease experience increased risk of relapse. Delineation of prognosis is desired to compare cohorts and outcomes between trials, and to define groups of pts for whom reduction in treatment may be appropriate or where novel therapeutic approaches are needed. The International Prognostic Score (IPS), which was derived from a discovery set of 1,618 HL pts with complete data, was a seminal publication in the field (Hasenclever and Diehl NEJM 1998). However, these data were published &gt;20 years ago with a significant minority of pts having received chemotherapy regimens no longer in clinical use. More contemporary analyses have shown altered utility of the IPS (e.g., Moccia JCO 2012; Diefenbach BJH 2015). In addition, prior studies identified bulk disease as an adverse prognostic factor in advanced stage HL (Laskar JCO 2004; Johnson JCO 2010). Our objective was to leverage individual pt data (IPD) from HoLISTIC (www.hodgkinconsortium.com) to discover a new, robust, and modern prognostication index for advanced-stage HL pts applicable to diverse settings across the world. Methods: We created a data repository of IPD from clinical trials for newly diagnosed HL pts, which includes 4,085 advanced-stage (III or IV) pts treated in 8 large, prospective studies completed in the modern era (ie, IIL HD9601: Gobbi JCO 2005; Italian HD2000: Federico JCO 2009; ECOG 2496: Gordon JCO 2013; SWOG 0816: Press JCO 2016; IIL HD0801: Zinzani JCO 2016; RATHL: Johnson NEJM 2016; GITIL HD0607: Gallamini JCO 2017; and COG AHOD 0831: Kelly BJH 2019) as well as prominent cancer registries (eg, the Mayo/Iowa Molecular Epidemiologic Resource (MER)). The discovery analysis herein included pts from the ECOG 2496, HD0801 IIL, GITIL HD0607, and SWOG 0816 studies. Furthermore, it was restricted to pts (n=1,279) on these trials with complete data for all 9 covariates of interest: age; sex; advanced stage (III vs IV); B symptoms; any bulk; and values of hemoglobin, white blood count (WBC), lymphocyte count, and albumin. Using Cox proportional hazard (PH) models, we evaluated univariate associations between 5-year progression-free survival (PFS) and overall survival (OS) with the aforementioned prognostic variables. Age was categorized based on plots and optimum model fit (c statistic). Lab values were dichotomized using cut-points from the 1998 IPS. Per convention, treatment factors were not included in the model. To identify independent prognostic factors of PFS and OS, a parsimonious Cox PH model was fit using backward selection of all potential risk factors (P&lt;0.05). Hazard ratios (HR) with 95% confidence interval (CI) were reported. Kaplan Meier (KM) plots were also reported for risk factors in the multivariable (MVA) models to visualize differences. Results: Among all pts, characteristics included: median age of 32.9 years (IQR 25.4-44, range 15-83); 55% male; 49% stage IV; 63% B symptoms; 26% bulk &gt;10 cm; 20% hemoglobin &lt;10.5 g/dL; 15% WBC count ≥15,000/mm3; 9% lymphocyte count &lt;600/mm3; and 63% with albumin &lt;4g/dL. For analysis of age, we observed a U-shaped relationship with PFS (Fig A), which helped delineate optimal cut points of 15-24 years (23.1%), 25-49 years (61.4%), and ≥50 years (15.6%). In univariate analysis: age, stage, B symptoms, bulk, anemia, low lymphocyte count, and low albumin were associated with worse survival. In the MVA model, age &gt;50 years, stage IV disease, B symptoms, and bulky disease were associated with worse PFS; and age &gt;50 years, stage IV disease, bulky disease, anemia, and low albumin were associated with worse OS (Fig B). KM plots for age, stage, and bulk are presented in Fig C. Conclusions. In this international, multi-study analysis of advanced stage HL in the modern era, we identified several factors that were associated with both worse PFS and OS on MVA (ie, age, stage IV disease, and bulky disease). The finding of bulky disease as a significant prognostic factor warrants further investigation. In addition, we detected an age-related U-shaped impact on PFS with inferior outcomes for pts ages 15-25 years and ≥50 years, the latter in an increasing linear fashion. Altogether, these data will serve as a training cohort for a modern HL prognostication index that will be augmented and analyzed with a large independent validation cohort (vis-à-vis the remaining HL data in the HoLISTIC consortium), which will be presented at the ASH meeting. Disclosures Parsons: Seattle Genetics: Consultancy. Advani:Astra Zeneca, Bayer Healthcare Pharmaceuticals, Cell Medica, Celgene, Genentech/Roche, Gilead, KitePharma, Kyowa, Portola Pharmaceuticals, Sanofi, Seattle Genetics, Takeda: Consultancy; Celgene, Forty Seven, Inc., Genentech/Roche, Janssen Pharmaceutical, Kura, Merck, Millenium, Pharmacyclics, Regeneron, Seattle Genetics: Research Funding. Federico:Spectrum: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sandoz: Consultancy, Membership on an entity's Board of Directors or advisory committees; Mundipharma s.r.l.: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium/Takeda: Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Cephalon/Teva: Research Funding. Friedberg:Acerta Pharma - A member of the AstraZeneca Group, Bayer HealthCare Pharmaceuticals.: Other; Bayer: Consultancy; Kite Pharmaceuticals: Research Funding; Portola Pharmaceuticals: Consultancy; Roche: Other: Travel expenses; Seattle Genetics: Research Funding; Astellas: Consultancy. Hutchings:Genmab: Research Funding; Janssen: Research Funding; Roche: Consultancy; Genmab: Consultancy; Takeda: Consultancy; Roche: Research Funding; Celgene: Research Funding; Daiichi: Research Funding; Sankyo: Research Funding; Novartis: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; Roche: Honoraria; Genmab: Honoraria; Takeda: Honoraria. Johnson:MorphoSys: Honoraria; Kymera: Honoraria; Kite Pharma: Honoraria; Incyte: Honoraria; Celgene: Honoraria; Epizyme: Consultancy, Research Funding; Novartis: Honoraria; Takeda: Honoraria; Oncimmune: Consultancy; Boehringer Ingelheim: Consultancy; Janssen: Consultancy; Oncimmune: Consultancy; Janssen: Consultancy; Genmab: Honoraria; Bristol-Myers: Honoraria; Epizyme: Consultancy, Research Funding. Radford:Novartis: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Speakers Bureau; ADCT: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Research Funding; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; GlaxoSmithKline: Current equity holder in publicly-traded company, Other: Spouse; AstraZeneca: Current equity holder in publicly-traded company, Other: Spouse; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Zinzani:MSD: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Incyte: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees; EUSA Pharma: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sandoz: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Immune Design: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Eusapharma: Consultancy, Speakers Bureau; Verastem: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kyowa Kirin: Consultancy, Speakers Bureau; TG Therapeutics, Inc.: Honoraria, Speakers Bureau; Kirin Kyowa: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Servier: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen-Cilag: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celltrion: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; ADC Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Portola: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Immune Design: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Merck: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding. Evens:Abbvie: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria, Research Funding; MorphoSys: Consultancy, Honoraria; Research To Practice: Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria; Epizyme: Consultancy, Honoraria, Research Funding; Merck: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria; Mylteni: Consultancy, Honoraria.