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  • 1
    In: The Lancet Oncology, Elsevier BV, Vol. 23, No. 1 ( 2022-01), p. 149-160
    Type of Medium: Online Resource
    ISSN: 1470-2045
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
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  • 2
    In: npj Breast Cancer, Springer Science and Business Media LLC, Vol. 3, No. 1 ( 2017-09-13)
    Abstract: There are few medical issues that have generated as much controversy as screening for breast cancer. In science, controversy often stimulates innovation; however, the intensely divisive debate over mammographic screening has had the opposite effect and has stifled progress. The same two questions—whether it is better to screen annually or bi-annually, and whether women are best served by beginning screening at 40 or some later age—have been debated for 20 years, based on data generated three to four decades ago. The controversy has continued largely because our current approach to screening assumes all women have the same risk for the same type of breast cancer. In fact, we now know that cancers vary tremendously in terms of timing of onset, rate of growth, and probability of metastasis. In an era of personalized medicine, we have the opportunity to investigate tailored screening based on a woman’s specific risk for a specific tumor type, generating new data that can inform best practices rather than to continue the rancorous debate. It is time to move from debate to wisdom by asking new questions and generating new knowledge. The WISDOM Study (Women Informed to Screen Depending On Measures of risk) is a pragmatic, adaptive, randomized clinical trial comparing a comprehensive risk-based, or personalized approach to traditional annual breast cancer screening. The multicenter trial will enroll 100,000 women, powered for a primary endpoint of non-inferiority with respect to the number of late stage cancers detected. The trial will determine whether screening based on personalized risk is as safe, less morbid, preferred by women, will facilitate prevention for those most likely to benefit, and adapt as we learn who is at risk for what kind of cancer. Funded by the Patient Centered Outcomes Research Institute, WISDOM is the product of a multi-year stakeholder engagement process that has brought together consumers, advocates, primary care physicians, specialists, policy makers, technology companies and payers to help break the deadlock in this debate and advance towards a new, dynamic approach to breast cancer screening.
    Type of Medium: Online Resource
    ISSN: 2374-4677
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
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  • 3
    In: Radiology: Imaging Cancer, Radiological Society of North America (RSNA), Vol. 5, No. 4 ( 2023-07-01)
    Type of Medium: Online Resource
    ISSN: 2638-616X
    Language: English
    Publisher: Radiological Society of North America (RSNA)
    Publication Date: 2023
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  • 4
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 4_Supplement ( 2022-02-15), p. P2-01-03-P2-01-03
    Abstract: Background: Identifying mechanisms that govern the shedding of ctDNA in blood could inform the use of liquid biopsy in individual patients. Previous studies in the I-SPY2 neoadjuvant trial involving high-risk breast cancer showed that the detection of ctDNA before treatment was associated with aggressive clinical characteristics and residual ctDNA after treatment was associated with poor outcomes. Moreover, ctDNA positivity rates significantly varied across breast cancer subtypes suggesting that ctDNA shedding may in part be driven by subtype-specific etiology. We performed genome-wide transcriptomic analysis to identify genes and biological processes associated with increased ctDNA shedding within and across receptor subtypes. Methods: Our study involved 227 patients in I-SPY2 with tumor gene expression and ctDNA data at pretreatment. All patients were at high risk for recurrence (MammaPrint high). Each subtype: HR+HER2- (n=109), HER2+ (n=19), and triple negative breast cancer (TNBC, n=99) was evaluated independently. We performed differential expression (DE) analysis on the global transcriptome (m=19,134 genes) and curated gene signature (cGS, m=31 signatures developed in I-SPY2) data between ctDNA+ and ctDNA- patients at baseline. Gene-set enrichment analysis (GSEA) was also performed across hallmark (H, m=50), canonical pathway (CP, m=5,501), gene ontology (GO, m=9,996) and immunologic (IM, m=4,872) gene sets. Features were associated with ctDNA shedding if Benjamini-Hochberg adjusted p & lt; 0.05. For subtypes with smaller sample size and unbalanced groups, we also report features with nominally significant p & lt; 0.05. Results: ctDNA positivity rate was significantly higher in TNBC (91%) than in HR+HER2- and HER2+ (65% and 74% respectively, Fisher p & lt;0.001). The HR+HER2- subtype had the most significant hits for DE analysis between ctDNA+ and ctDNA- patients, with 0.2% of genes and 3.2% of cGS. No genes or cGS were differentially expressed in TNBC and HER2+, likely due to imbalance or small size of these groups. For GSEA, we observed the most significant number of enrichments in HR+HER2- subtype, with 58%, 21.8%, 4.4% and 40.3% of H, CP, GO, and IM gene sets enriched, respectively. In the HER2+ subtype, 40% H, 15.7% CP and 36.4% IM gene sets were significantly enriched, while no gene sets were enriched in TNBC. To identify common mechanistic themes across subtypes, we also considered nominally significant features in DE and GSEA. Processes associated with infection and innate immune responses were enriched in ctDNA+ patients, while adaptive immune response and antigen presentation—e.g., T-cell, TCR and MHC II protein complex, and downregulation of MYC targets were enriched in ctDNA- patients. HR+HER2- and HER2+ subtypes shared the most common modulated features with 134 genes and 2,165 gene sets, including up-regulation of cell cycle and proliferation in ctDNA+ patients, as well as up- or down-regulation of specific immunologic and metabolic processes. In contrast, TNBC gene set enrichment was associated with more distinct biologic processes, sharing common enrichment of 113 and 27 gene sets with HR+HER2- and HER2+ subtype, respectively. Conclusions: Findings from our exploratory analysis suggest a key role of immune response pathways in the control of ctDNA release. Additionally, tumor cell proliferation was associated with increased shedding in HR+HER2- and HER2+ subtypes, while down regulation of MYC targets was associated with ctDNA- patients across all subtypes. These suggest an important role of cell cycle in ctDNA shedding. Overall, our analysis revealed common and unique mechanisms potentially associated with ctDNA shedding across and within subtypes. However, due to the unbalanced groups and limited sample sizes, validation in a larger cohort is warranted. Citation Format: Rosalyn W. Sayaman, Denise M. Wolf, Christina Yau, Lamorna Brown Swigart, Gillian L. Hirst, Laura Sit, Nicholas O'Grady, Amy L. Delson, I-SPY2 Investigators, Laura Esserman, Laura J. van 't Veer, Mark Jesus M. Magbanua. Elucidating the biology of circulating tumor DNA (ctDNA) shedding across receptor subtypes in high-risk early-stage breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-01-03.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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  • 5
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 81, No. 4_Supplement ( 2021-02-15), p. PD14-02-PD14-02
    Abstract: Background: Preclinical studies suggest synergy between PARP inhibitors and immune checkpoint inhibitors. In the I-SPY 2 TRIAL, the anti-PDL1 therapeutic antibody durvalumab combined with the PARP inhibitor olaparib showed increased efficacy relative to control in both the HR+/HER2- and TN subtypes. Pre-specified biomarker analysis was performed to test 7 immune genes/signatures previously associated with response to pembrolizumab [Pembro] and/or durvalumab and a DNA Repair Deficiency (DRD) signature previously associated with response to veliparib/carboplatin, as specific predictors of response to durvalumab/olaparib [Durva] . We also assessed MammaPrint (MP) High1/(ultra)High2 risk class (MP1/2), a prognostic signature used in the trial’s adaptive randomization engine, and performed exploratory analysis on additional signatures. Methods: 105 patients (Durva: 71, controls: 34) had Agilent 44K gene expression from FFPE pre-treatment biopsies and pCR data; and 370 (Durva: 71, controls: 299) had MP1/2 and pCR data. We evaluated 13 genes/signatures (10 immune, 1 DRD, 1 ER, 1 proliferation) and MP1/2 as biomarkers of Durva response, using logistic modeling to assess performance. A biomarker is considered a specific predictor of Durva response if it associates with response in the Durva arm, and if the biomarker x treatment interaction is significant (likelihood ratio test, p & lt;0.05). pCR rates within MP1/2 classes are estimated using Bayesian logistic modeling. Analysis is also performed adjusting for HR status as a covariate, and numbers permitting, within receptor subsets. Our statistics are descriptive rather than inferential and do not adjust for multiplicities. Results: 8/10 immune biomarkers, including the genes PD1 and PDL1, and B-cell, dendritic cell and mast cell (but not T-cell or CD68) signatures associate with response to Durva in the population as a whole and in a model adjusting for HR status. As seen in previous immunotherapy trials, higher levels generally associate with pCR, with the exception of the mast cell signature, where high levels associate with non-response as was also shown for Pembro (I-SPY 2). In addition, high levels of the DRD (PARPi7) and proliferation signatures associate with response, as do low levels of ER signaling (ESR1/PGR average). Many of these biomarkers also associate with response in the control arm, and for no immune biomarker is the treatment interaction significant, suggesting a lack of predictive specificity. In subset analysis, 13/14 biomarkers (all but CD68) predict Durva response in the HR+/HER2- subset, with the strongest association to pCR being a low level of ESR1/PGR (p=2E-08). In our Bayesian analysis, the difference in estimated pCR rates between arms are primarily observed in the MP2 subtype, particularly in the HR+/HER2- MP2 patients (estimated pCR rate of 64% in Durv vs 22% in Ctr). In the TN subset, only 3/14 biomarkers associate with response: the STAT1 and TAM/TcCassII-ratio signatures that also associate with durvalumab response in a prior study (NCT02489448) and, interestingly, the proliferation signature. Notably, the dendritic, T-cell and tumor inflammatory signatures (TIS) predicting TN response to Pembro (I-SPY2, GeparSixto) do not associate with Durva response in TNBC, suggesting differences in the biology underlying response to PD1 and PDL1 inhibitors. Conclusion: Multiple immune, DRD, proliferation, and ER signatures associate with response to durvalumab/olaparib therapy, but many lack predictive specificity. MP2 class and/or low ESR1/PGR are the strongest predictors of pCR in the HR+/HER2- subset; whereas for TNs, cytokine- and monocyte-dominated immune signatures like STAT1 [PMID: 19272155] and TAM/TcClassII ratio [PMID: 24205370] are most predictive of response. These results require validation. Citation Format: Denise M Wolf, Christina Yau, Lamorna Brown-Swigart, Nick O'Grady, Gillian Hirst, Laura Sit, I-SPY 2 TRIAL Consortium, Smita Asare, Don Berry, Laura Esserman, Hyo Han, Lajos Pusztai, Laura van 't Veer. Biomarkers predicting response to durvalumab combined with olaparib in the neoadjuvant I-SPY 2 TRIAL for high-risk breast cancer [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 PD14-02.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
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  • 6
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 81, No. 4_Supplement ( 2021-02-15), p. PD9-04-PD9-04
    Abstract: Background: The goal of I-SPY 2 is to rapidly test novel therapies in addition to standard of care in high-risk breast cancer patients. It has resulted in increasing response rates, where pCR rates in TNBC and HR-HER2+ subsets have reached ~60% and ~75%, respectively. Yet, there remains a sizeable subset of non-responders, especially among HR+ patients. Identification of ‘universal’ resistance mechanisms may guide rational selection of agents to improve these patient's outcomes. Thus, we analyzed reverse phase protein array (RPPA) based quantitative protein/phosphoprotein data across arms to assess whether there are common mechanisms rendering these cancers resistant to all agent classes tested to date. Methods: 736 patients (260 HR+HER2-, 252 TN, 142 HR+HER2-, and 82 HR-HER2+; over 8 arms: 194 Ctr, 105 neratinib (N), 63 veliparib/carboplatin (VC), 128 AMG386 (anti-ANG1/2), 87 MK2206 (anti-AKT), 43 TH/pertuzumab (P), 49 TDM1/P, and 67 pembrolizumab (Pembro)) with pCR and RPPA data at the pre-treatment time point were considered for this analysis. 141 RPPA endpoints representing key cancer pathways were assessed for association with pCR using logistic regression modeling, with HR, HER2 and treatment arm as covariates (likelihood ratio test; p & lt;0.05). Analysis was also performed in HR/HER2 subsets and within treatment arms. Markers were analyzed individually; multiple comparison correction (Benjamini-Hochberg) was applied to p-values. Our analysis is exploratory, and does not adjust for other biomarkers outside this study. Results: Prior to FDR correction, high levels of Cyclin D1, a cell cycle protein implicated in estrogen-mediated DNA damage repair, associate with non-pCR in the population as a whole and within all subtypes except for the HR-HER2+ subset; an association that retains significance after FDR correction overall as well as in HER2- and HR+HER2- subsets. Within individual arms, high Cyclin D1 predicted non-response in VC, control, and AMG386; and trends toward association in Pembro and N. In addition, high quantitative ER and phospho-androgen receptor (pAR; S650) associate with non-pCR in the population as a whole and in the HR+HER2- subset. For both ER and pAR the strongest association with non-pCR was in the Pembro arm. Candidates for universal sensitivity signals include immune proteins JAK-STAT (pSTAT5 (Y694) and pSTAT1 (Y701)) overall; and pERBB2/pEGFR for HER2+ patients. Conclusions: High levels of Cyclin D1, but not other cell cycle proteins, predict non-response to chemo-/targeted-therapy across arms and subtypes, suggesting that agents specifically targeting Cyclin D1 may increase chemo-sensitivity. ER/phospho-AR as global resistance signals suggest inclusion of anti-AR agents in combination therapy, and the need for new endocrine-based approaches. Citation Format: Julia Wulfkuhle, Denise Wolf, Christina Yau, Lamorna Brown-Swigart, Rosa I Gallagher, Gillian Hirst, Laura Sit, Smita Asare, I-SPY 2 TRIAL Investigators, Nola Hylton, Angela DeMichele, Douglas Yee, Jo Chien, Hope Rugo, John Park, Kathy Albain, Rita Nanda, Debu Tripathy, Richard Schwab, Don Berry, Laura Esserman, Laura van t' Veer, Emanual Petricoin, III. Identification of biomarkers associated with therapeutic resistance: Quantitative protein/phosphoprotein analysis of ~750 patients across 8 arms of the neoadjuvant I-SPY 2 TRIAL for high-risk early stage breast cancer [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 PD9-04.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
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  • 7
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 81, No. 4_Supplement ( 2021-02-15), p. PS11-04-PS11-04
    Abstract: Introduction: One of the principal limiting factors to achieving cures in patients with cancer is drug resistance. Drug repositioning is the application of FDA-approved drug compounds for novel indications beyond the scope of the drug’s original intended use. This approach offers advantages over traditional drug development by reducing development costs and providing shorter paths to approval, as drug safety has already been established during the drug’s original regulatory process. One approach for computational drug repositioning involves generating a disease gene expression signature and then identifying a drug that can reverse this disease signature. In this study, we extracted drug resistance signatures from the I-SPY 2 TRIAL by comparing gene expression profiles of responder and non-responder patients stratified by treatment and molecular subtype. We then applied our drug repositioning pipeline to predict compounds that can reverse the gene expression profiles of these drug resistance signatures. We hypothesize that reversing these drug resistance signatures will resensitize tumors to treatment and improve patient outcome. Methods: We first generated the drug resistance signatures by performing differential expression between responders (RCB 0/I) and non-responders (RCB III) within treatment arms and molecular subtypes. An optimal log fold-change cutoff was selected for each signature by identifying the cutoff that best separates the responder and non-responder samples using k-means clustering. We then applied our drug repositioning pipeline to identify compounds that significantly reverse these signatures using the drug perturbation profiles generated in a breast cancer cell line in the Connectivity Map v2 dataset. Briefly, the pipeline uses a non- parametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov (KS) statistic to assess the enrichment of resistance genes in a ranked drug gene expression list. Significance of each prediction is estimated from a null distribution of scores generated from random gene signatures. Results: We found that few individual genes are shared among the resistance signatures across the treatment arms and molecular subtypes, with the most common genes present in only 5/17 of the treatment arm and molecular subtype groups. At the pathway-level, however, we found that immune-related pathways are generally enriched among the responders and estrogen-response pathways are generally enriched among the non-responders. Although most of our drug predictions are unique to treatment arms and molecular subtypes, our drug repositioning pipeline identified the selective estrogen receptor degrader (SERD) fulvestrant as a compound that can potentially reverse resistance across a majority of the treatment arms and molecular subtypes. Conclusion: We applied our drug repositioning pipeline to identify novel agents to sensitize drug-resistant tumors in the I-SPY 2+ clinical trial and identified a SERD, fulvestrant, as a potential candidate for multiple molecular subtypes and treatment arms. Citation Format: Katharine Yu, Amrita Basu, Christina Yau, Denise Wolf, Gillian Hirst, Laura Sit, Nicholas O’Grady, Thelma Brown, I-SPY 2 TRIAL Investigators, Angela DeMichele, Don Berry, Nola Hylton, Doug Yee, Laura Esserman, Laura van 't Veer, Marina Sirota. Computational drug repositioning for the identification of new agents to sensitize drug-resistant breast tumors across treatment arms and molecular subtypes [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 PS11-04.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
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  • 8
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 4_Supplement ( 2020-02-15), p. PD9-04-PD9-04
    Abstract: Background: In an adaptive randomized trial, when new treatment combinations are being tested, it is important to be able to identify patients who are progressing on treatment so that they can be changed to a different therapeutic regimen. We know that even within the molecularly high risk patients in I-SPY 2, there is considerable variation in biology. In this study, we will present results of using MRI-calculated functional tumor volume (FTV) to identify tumor progression for each breast cancer subtype. Methods: Patients (n=990) enrolled in the I-SPY 2 TRIAL who were randomized to the graduated experimental drug arms or controls from 2010 to 2016 were analyzed. Four MRI exams were performed for each patient: pre-NAC (T0), after 3 weeks of NAC (T1), between regimens (T2), and post-NAC (T3). Functional tumor volume (FTV) was calculated at each exam by summing voxels meeting enhancement thresholds. Tumor progression at T1, T2 or T3 was identified by a positive FTV change relative to T0. Visual inspection was used to exclude false progression due to strong background parenchymal enhancement post-contrast, prominent vessels, motion, or insufficient image quality. pCR was defined as no invasive disease in the breast and lymph nodes. Negative predictive value for pCR was defined as:NPV=number of true non-pCRs / number of patients with MRI assessed tumor progressions, where “true non-pCRs” referred to patients who were non-pCRs at surgery and were assessed as progressors by MRI. The analysis was performed in the full cohort and in sub-cohorts defined by HR and HER2 statuses. Results: Out of 990 patients, 878 had pCR outcome data (pCR or non-pCR, pCR rate = 35%). Total and non-pCR numbers for each subtype, number of patients with tumor progression assessed by MRI at T1, T2, and T3, and NPVs, are shown in Table 1. In the full cohort, the NPV increased consistently over treatment, from T1 (NPV=83%) to T2 (93%), and to T3 (100%). The HER2+ cancer subtypes showed fewer MRI-assessed tumor progressions than HER2- subtypes: e.g. 10/209 (5%) vs. 108/669 (16%) at T1. NPV was 100% for HER2+ subtypes at T1 and T2 except for a single misclassification of a HR- tumor at T1. Only 6 tumor progressors, all HER2- were identified at T3, and all were confirmed at surgery as non-pCRs (NPV=100%). For HR+/HER2-, the NPV increased slightly from 89% at T1 to 91% at T2, while triple negative subtype had a more substantial increase, from 78% to 92%. Conclusions: Our study showed strong association between tumor progressors assessed by MRI with true non-pCRs after NAC. For HER2+ tumors, although MRI progressors are rare, they strongly indicate non-pCR at all treatment time points, while HER2- subtypes show more accurate results later in treatment. We are evaluating MRI change at 6 weeks to determine if that time point is sufficient to predict progressors. Table 1 MRI assessed tumor progression at different treatment time pointN/non-pCRs/%non-pCRMRI assessed tumor progressionT1 (after 3 weeks)T2 (inter-regimen)T3 (post-NAC)NNPV (%)NNPV (%)NNPV (%)Full cohort878/572/65%11883.14192.76100%HR+/HER2-344/280/81%4588.91190.93100%HR+/HER2+134/85/63%610021000N/AHR-/HER2+75/23/31%47521000N/Atriple negative325/184/57%6377.82692.33100% Citation Format: Wen Li, Natsuko Onishi, David C Newitt, Jessica Gibbs, Lisa J Wilmes, Ella F Jones, Bonnie N Joe, Laura S Sit, Christina Yau, A. Jo Chien, Elissa Price, Kathy S Albain, Theresa Kuritza, Kevin Morley, Judy C Boughey, Kathy Brandt, Sadia Choudhery, Amy S Clark, Mark Rosen, Elizabeth S McDonald, Anthony D Elias, Dulcy Wolverton, Kelly Fountain, David M Euhus, Heather S Han, Bethany Niell, Jennifer Drukteinis, Julie E Lang, Janice Lu, Jane L Meisel, Zaha Mitri, Rita Nanda, Donald W Northfelt, Tara Sanft, Erica Stringer-Reasor, Rebecca K Viscusi, Anne M Wallace, Douglas Yee, Rachel Yung, Smita M Asare, Michelle E Melisko, Jane Perlmutter, Hope S Rugo, Richard Schwab, W. Fraser Symmans, Laura J van't Veer, Donald A Berry, Angela DeMichele, Hiroyuki Abe, Deepa Sheth, Kirsten K Edmiston, Erin D Ellis, Richard Ha, Ralph Wynn, Erin P Crane, Charlotte Dillis, Michael Nelson, An Church, Claudine Isaacs, Qamar J Khan, Karen Y Oh, Neda Jafarian, Dae Hee Bang, Christiane Mullins, Stefanie Woodard, Kathryn W Zamora, Haydee Ojeda-Fornier, Pulin Sheth, Linda Hovanessian-Larsen, Mohammad Eghtedari, Michael Spektor, Marina Giurescu, Mary S Newell, Michael A Cohen, Elise Berman, Constance Lehman, William Smith, Kim Fitzpatrick, Marisa H Borders, Wei Yang, Basak Dogan, Sally Goudreau, Thelma Brown, Laura J Esserman, Nola M Hylton. Breast cancer subtype specific association of pCR with MRI assessed tumor volume progression during NAC in the I-SPY 2 trial [abstract] . In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD9-04.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
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  • 9
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 4_Supplement ( 2022-02-15), p. P5-13-12-P5-13-12
    Abstract: Background: Pembrolizumab, an anti-PD-1 immune checkpoint inhibitor, is approved for treatment in multiple cancers and has been shown to increase pathologic complete response (pCR) and survival in the neoadjuvant setting in breast cancer. Pembrolizumab combined with paclitaxel followed by doxorubicin/cyclophosphamide (P+T- & gt;AC) was evaluated in HER2- patients in the neoadjuvant I-SPY 2 TRIAL and graduated in the HER2-, HR+HER2- and triple negative (TN) signatures. Our biomarker analysis revealed that immune cell abundance and MP2 class predicts response in HR+HER2- patients whereas tumor-immune proximity scores (multiplex-IF) and signaling signatures (mRNA) predict response in TN patients. In an effort to further improve response, the TLR9 agonist SD101 was added to Pembro (P+S+T - & gt; AC) for testing in I-SPY 2. While P+S increased estimated pCR rates relative to control (T- & gt;AC), it did not graduate for efficacy. To better understand the biology underlying response to P+S, we evaluated 31 expression based biomarkers relating to immune, ER and proliferation as predictors of response to P+S overall and within subtypes. Methods: Data from 72 patients (HR+HER2-: 45; TN: 27) treated with P+S were available. Pre-treatment FFPE biopsies were assayed using Agilent gene expression arrays. We evaluated genes/signatures representing 6 immune checkpoint/targets (CD274, PDCD1, TLR9, TIGIT, LAG3, and TIM3), 14 immune cell types (e.g., TILs, T cells, CD8 T cells, Tregs, cytotoxic cells, dendritic cells, mast cells, B cells, macrophages, and neutrophils), 3 T/B-cell prognostic (e.g., ICS5), 5 Tumor-immune signaling (e.g., STAT1, Chemokine12, TIS, and Geparsixto), and 1 TGFB signaling signatures as predictors of response to P+S. We also assess ESR1/PGR and proliferation, and the prognostic marker MP2 class. Signature scores were calculated as previously published. We used logistic modeling to assess biomarker performance (likelihood ratio test, p & lt;0.05). This analysis was also performed in a model adjusting for HR status, and within receptor subsets. For the dichotomous MP1/2, we used Bayesian modeling to estimate the pCR rates of patients in each class. Multiple hypothesis testing adjustment was performed using the Benjamini-Hochberg method. Our statistics are descriptive rather than inferential and do not adjust for multiplicities of other biomarkers outside this study. Results: Higher levels of most (24/29) immune biomarkers associate with pCR in the population as a whole (BH LR p & lt;0.05). Among target genes, CD274 and PDCD1 strongly associate with pCR; however, TLR9 did not associate with response. As seen in previous biomarker analyses of IO agents including Pembro, there are major differences in predictive biology between receptor subsets. Immune cell subpopulation abundance signatures (13/14) and T/B-cell prognostic signatures (3/3) associate with pCR in HR+HER2- but not TN subsets. Whereas tumor-immune signaling signatures (4/5) dominated by chemokines and cytokines associate with pCR in both HR+HER2- and TN subsets. In addition, high ESR1/PGR and low proliferation signature levels associate with response in HR+HER2-, as does MP1/2 class, with an estimated 45% pCR in MP2 versus 17% pCR in MP1. Analysis of multiplex-IF immune markers is pending. Conclusion: Though TN patients are more responsive to Pembro+SD101 and other immunotherapies than HR+HER2- patients, many more immune biomarkers associate with pCR in the latter group. Only tumor-immune signaling signatures associate with pCR in both HR+HER2- and TN subsets. Response in the HR+HER2- subset is higher in MP2 class, high-proliferation, lower-ER tumors. Lack of predictive signal for TLR9 may help explain why the addition of SD101 to Pembro failed to further boost response. Citation Format: Denise M Wolf, Christina Yau, Michael Campbell, Hatem Soliman, Mark Magbanua, Ruixiao Lu, Nicholas O'Grady, Lamorna Brown-Swigart, Gillian Hirst, Laura Sit, Yvonne M Florence, I-SPY 2 TRIAL Investigators, Smita Asare, Doug Yee, Angie DeMichele, Don Berry, Laura Esserman, Jo Chien, Laura van 't Veer. Immune signatures and MammaPrint (ultra) high risk class (MP2) as predictors of response to pembrolizumab combined with the TLR9 agonist SD101 in the neoadjuvant I-SPY 2 TRIAL [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-13-12.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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  • 10
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 4_Supplement ( 2022-02-15), p. PD10-07-PD10-07
    Abstract: Background: The CK12 expression signature consists of genes CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, CXCL13 and was previously shown to associate with the presence of T and B cell rich tertiary lymphoid structures in melanoma and other cancers, and with better patient survival independent of tumor staging and treatment. I-SPY 2 is a biomarker-rich, phase II neoadjuvant platform trial for high risk early stage breast cancer. Here we leverage the I-SPY 2 biomarker program to test the hypothesis that this signature associates with sensitivity to neoadjuvant immunotherapies and potentially other classes cancer therapeutics in breast cancer. Methods: Data from 1130 patients across 12 arms of I-SPY2 (control (ctr): 210; veliparib/carboplatin (VC): 71; neratinib (N): 114; MK2206: 93; ganitumab: 106; ganetespib: 93; AMG386: 134; TDM1/pertuzumab(P): 52; H/P: 44; pembrolizumab (pembro): 69; durvalumab/olaparib (durva/olap): 71; pembro/SD101: 72) were available for analysis. Pre-treatment FF (n=987) or FFPE (n=143) biopsies were assayed using Agilent gene expression arrays. Signature scores were calculated as the average expression level across the 12 genes, after z-score normalization. We used logistic modeling to assess association with pCR in each arm in a model adjusting for HR and HER2 (likelihood ratio test, p & lt;0.05). This analysis was also performed within HR/HER2 receptor subsets, numbers permitting. We also assessed differences in levels across HR/HER2 subsets using ANOVA and Tukey post-hoc testing. Our statistics are descriptive rather than inferential and do not adjust for multiplicities of other biomarkers outside this study. Results: CK12 levels associate with HR/HER2 status (ANOVA p=1.07E-14), with higher levels in TN and HR-HER2+ subsets and lower levels in HR+ groups. Overall, patients with higher levels of CK12 were significantly more likely to achieve pCR in all 3 IO arms: pembro (OR=3.4/1SD), pembro/SD101 (OR=4/1SD), and durva/olaparib (OR=2.5/1SD) (LR p & lt;0.05), in a model adjusting for HR status. The CK12 performed favorably in predicting response to pembro/SD101 compared to several other genomic signatures measuring intratumoral immune response. Higher CK12 also associates with response to the ANG1/2 inhibitor AMG386, an agent known to have immune modulatory activity. Higher CK12 was moderately associated with pCR in the control (OR=2.0/1SD), neratinib (OR=1.7/1SD), veliparib/carboplatin (OR=2.0/1SD), ganitumab (OR= 1.7/1SD) and TDM1/P arms (OR=2.1/1SD). Within the HR+HER2- subset, CK12 associated with pCR in all three IO arms, and in the control, AMG386, ganitumab, and ganetespib arms. Within the smaller TN subset, it associated with response in pembro and pembro/SD101 arms but not in durva/olaparib, and in the neratinib and AMG386 arms. Chemokine12 mostly did not associate with pCR in HER2+ subsets, except for HR+HER2+ patients treated with neratinib, and HR-HER2+ patients in the original control arm (trastuzumab). Conclusion: The CK12 signature is highly predictive of complete pathologic response to immuno-oncology agents and other therapeutics supporting the role of the crosstalk within the tumor immune microenvironment in predicting response across subtypes. This gene expression signature can be readily obtained from microarrays and warrants further investigation in future arms of ISPY2 as a predictive biomarker. Citation Format: Hatem Soliman, Denise Wolf, Jo Chien, Christina Yau, Michael Campbell, Mark Magbanua, Ruixiao Lu, Nicholas O'Grady, Lamorna Brown-Swigart, Gillian Hirst, Beverly Parker, Laura Sit, Smita Asare, Doug Yee, Angie DeMichele, Rita Nanda, Lajos Pusztai, Don Berry, Laura Esserman, Laura Van't Veer. Chemokine12 (CK12) tertiary lymphoid gene expression signature as a predictor of response in 3 immunotherapy arms of the neoadjuvant ISPY 2 TRIAL - pembrolizumab with and without SD101, and durvalumab combined with olaparib - and in 9 other arms of the trial including platinum-based and dual-anti-HER2 therapies [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD10-07.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Location Call Number Limitation Availability
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