Correction to: In vivo digestibility of six selected fodder species by goats in northern Ghana

The article "In vivo digestibility of six selected fodder species by goats in northern Ghana," written by F. K. Avornyo et al., was originally published electronically on the publisher's internet portal (currently SpringerLink) on 13 July 2019 without open access.

In vivo digestibility of six selected fodder species by goats in northern Ghana

An in vivo digestibility trial was conducted to evaluate the digestibility of six forages. These were selected indigenous browse species and groundnut haulms in Lawra and Jirapa Districts of the Upper West Region of Ghana. Groundnut haulms served as a control due to its known good digestibility in small ruminants. Thirty West African Dwarf (WAD) young castrated billy goats with a mean age of 8 months were used. The average live body weight was 8.9 ± 0.1 kg. The animals were randomly assigned to the six treatments and replicated five times in a completely randomized design. They were confined in metabolism cages. The adaptation period was 3 weeks after which data was taken for 10 days. The treatments were the leaves and tiny twigs of T1 Annona senegalensis, T2 Ficus gnaphalocarpa, T3 Pericopsis laxiflora, T4 Pterocarpus erinaceus, T5 Afzelia africana, and T6 (control) Arachis hypogaea. Feed intake, faecal matter and urine output were measured. The results indicated that dry matter (DM) contents of the feedstuffs were less than 50% but crude protein (CP) contents were higher than the minimum required for sustaining ruminants. Neutral and acid detergent fibre contents were high, and F. gnaphalocarpa, in particular, contained a higher level of phosphorus (P). The amounts of feed intakes were typical of most fodder species with the exception of P. laxiflora and P. erinaceus, which were lower (P < 0.05). The apparent digestibility values were also typical of most fodder species but the digestibility of A. africana fibre appeared to be lower. Nitrogen retention was positive for all test species with the exception of P. laxiflora, which was negative. Mortality was recorded in animals on P. laxiflora, and the rate was high. Pericopsis laxiflora leaves, even though morphologically similar to the leaves of P. erinaceus, were not a suitable fodder. Ficus gnaphalocarpa appeared to be the overall best fodder species in terms of nutrient uptake.

Abstract PD4-04: Role of breast MRI in predicting pathologically negative nodes after neoadjuvant chemotherapy in cN0 patients in the I-SPY2 trial

Background

In clinically node-negative (cN0) breast cancer patients with triple negative (TN) and HER2+ disease and breast pathological complete response (breast pCR), low rates of nodal positivity after neoadjuvant chemotherapy (NAC) have been demonstrated. In these patients, the omission of surgical axillary staging has been proposed. However, this information is not routinely known preoperatively. We aimed to validate the correlation between pathologic breast response and pathologic nodal status, and evaluate the relationship between response of the breast tumor on MRI and pathologic nodal status after NAC in cN0 patients in the I-SPY2 trial.

Methods

We identified all patients with cT1-4 cN0 breast cancer prior to NAC from graduated arms of the I-SPY2 trial, a prospective neoadjuvant chemotherapy trial. Absence of residual disease post-NAC was defined as longest diameter (LD) of 0 mm on MRI. Breast pCR was defined as the absence of invasive tumor in the breast at surgery. Associations between ypN0 and patient, MRI, and tumor characteristics were assessed using chi-square tests and univariate regression.

Results

Of 365 cT1-4 cN0 patients included, 128 had HR+/HER2- tumors (35%), 60 HR+/HER2+ tumors (16%), 34 HR-/HER2+ tumors (9%) and 143 TN tumors (39%). Overall, 283 patients (78%) were ypN0 after NAC and 152 patients (42%) had a breast pCR. ypN0 rate was higher in patients with a breast pCR than those with residual disease (93% vs 66%, p&lt;0.001). Patients with HR-/HER2+ and TN tumors were more likely to be ypN0 (97% and 87% respectively) than patients with HR+/HER2- and HR+/HER2+ disease (66% and 71% respectively, p&lt;0.001). Other characteristics associated with ypN0 were tumor grade (grade I 57%, grade II 66%, grade III 84%; p=0.002), MammaPrint Classification (High Risk 1 68% and High Risk 2 87%; p&lt;0.001) and absence of residual tumor in the breast on MRI (87% vs 72% in patients with evidence of tumor on MRI post-NAC/pre-surgery; p=0.003).

In patients with HR-/HER2+, HR+/HER2+, HR-/HER2+ or TN disease and a breast pCR, ypN0 rate was respectively 82%, 96%, 96% and 97% (table 1). In patients with HR+/HER2-, HR+/HER2+, HR-/HER2+ or TN disease and with no evidence of residual disease in the breast on MRI, rate of ypN0 was 71%, 80%, 94% and 96% respectively.

Conclusion

In cT1-4 cN0 breast cancer patients with HR+/HER2+, HR-/HER2+ and TN tumors and a breast pCR, ypN0 rates after NAC are extremely high. In patients with HR-/HER2+ and TN tumors with no residual breast disease on MRI after NAC and pre-surgery, ypN0 rates are high enough to consider omission of axillary surgery. In patients with HR+ tumors, MRI is unsufficiently predictive for pathological response and can therefore not be used to select ypN0 patients. Research on the prediction of ypN0 in cN+ I-SPY2 patients is ongoing.

Nodal status in patients with pCR and absence of residual disease on MRI Number of positive nodesBreast Cancer Subtype0123AllBreast pCR     HR+/HER2-27(82)2(6)4(12)033(100)HR+/HER2+24(96)01(4)025(100)HR-/HER2+24(96)1(4)0025(100)TN67(97)2(3)0069(100)Absence of residual disease on MRI     HR+/HER2-24(71)7(21)3(9)034(100)HR+/HER2+16(80)3(15)01(5)20(100)HR-/HER2+15(94)1(6)0016(100)TN54(96)2(4)0056(100)

Citation Format: van der Noordaa ME, Esserman L, Yau C, Mukhtar R, Price E, Hylton N, Abe H, Wolverton D, Crane EP, Ward KA, Nelson M, Niell BL, Oh K, Brandt KR, Bang DH, Ojeda-Fournier H, Eghtedari M, Sheth PA, Bernreuter WK, Umphrey H, Rosen MA, Dogan B, Yang W, Joe B, van 't Veer L, Hirst G, Lancaster R, Wallace A, Alvaredo M, Symmans F, Asare S, Boughey JC, I-SPY2 Consortium. Role of breast MRI in predicting pathologically negative nodes after neoadjuvant chemotherapy in cN0 patients in the I-SPY2 trial [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr PD4-04.

Abstract P1-15-02: Withdrawn

This abstract was withdrawn by the authors.

Citation Format: Schwab R, Clark A, Yau C, Wolf D, Chien AJ, Majure M, Ewing C, Wallace A, Roesch E, Helsten T, Forero A, Stringer-Reasor E, Vaklavas C, Nanda R, Jaskowiak N, Boughey J, Haddad T, Han H, Lee C, Albain K, Isaacs C, Elias A, Ellis E, Shah P, Lang J, Lu J, Tripathy D, Kemmer K, Yee D, Haley B, Korde L, Edmiston K, Northfelt D, Viscusi R, Khan Q, I-SPY 2 Consortium, Symmans WF, Perlmutter J, Hylton N, Rugo H, Melisko M, Wilson A, Singhrao R, Asare S, van't Veer L, DeMichele A, Berry D, Esserman L. Withdrawn [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P1-15-02.

Abstract P3-10-14: LIV-1 expression in primary breast cancers in the I-SPY 2 TRIAL

Background: LIV-1 is an estrogen-inducible gene that has been implicated in epidermal-to-mesenchymal transition (EMT) in preclinical models of progression and metastasis. Its expression is associated with node-positivity in breast cancer; and has been detected in a variety of cancer types, including estrogen receptor positive breast cancers. SGN-LIV1A is a novel antibody drug conjugate targeting LIV-1 that is currently being evaluated in the I-SPY 2 TRIAL. In this pilot study, we evaluated LIV-1 levels by IHC within HR/HER2/MammaPrint (MP) defined subtypes among patients screening for the I-SPY 2 TRIAL and its correlation to microarray assessed LIV-1 expression levels.

Method: In a pilot study, LIV-1 IHC staining was performed by Quest Diagnostics on the pre-treatment samples of 38 patients screening for the I-SPY 2 TRIAL. Pre-treatment expression data generated on a custom Agilent 44K platform was also available. We summarized the LIV-1 H-Scores and percent (%)-positivity across the population and within HR/HER2/MP subtypes; and we assessed the Pearson correlation between LIV-1 H-Score and LIV-1 gene expression levels. In addition, we compared the pre-treatment LIV-1 expression levels within HR/HER2/MP subtypes across I-SPY 2 TRIAL patients from completed arms and their relevant controls (n=989) using ANOVA and post-hoc Tukey tests. Our statistics are descriptive rather than inferential; and does not take into account multiplicities of other biomarkers outside of this study.

Results: Of the 38 patients evaluated, 37 have LIV-1 %-positivity &gt; 0; and 18 (47%) have 100% LIV1 positivity. The median LIV-1 H-Score is 200; and 89% of patients (34/38) have moderate/high LIV-1 staining (with H-Score≥100). Of the 34 patients who proceeded onto the trial (and have known HR/HER2/MP status), 9 are triple negative, 19 are HR+HER2-, and 6 are HER2+. Due to our small sample size, we did not further subset the triple negative and HER2+ cases; but within the HR+HER2- patients, 10 are MP1 compared to 9 who are MP2 class. LIV1 H-Score appears highest within the HR+HER2-MP1 cases (median: 290), followed by the HER2+ (median: 216), then the HR+HER2-/MP2 (median: 155), and the TN (median: 120) subtype. LIV1 H-score is significantly correlated with LIV-1 mRNA expression levels (Rp=0.79, p&lt;0.0001). Consistent with these observations, LIV-1 pre-treatment expression levels are significantly higher in the HR+HER2-MP1 group relative to all other HR/HER2/MP defined subtypes (Tukey HSD p &lt; 0.0001) across the I-SPY 2 TRIAL population. The HR+HER2+MP1 group also have high LIV-1 expression levels.

Conclusion: Our result suggest that although LIV-1 expression differs by subtype, it is expressed at a moderate/high level in the majority of patients. The good correlation between IHC and array-based LIV-1 expression levels enables us to leverage the entire existing I-SPY 2 dataset and confirm the high rates of LIV-1 expression across the I-SPY 2 population. Further studies to evaluate LIV-1 expression as a biomarker of response to LIV-1 targeting therapies for the neoadjuvant treatment of breast cancer are warranted and ongoing in I-SPY 2.

Citation Format: Yau C, Brown-Swigart L, Asare S, I-SPY 2 TRIAL Consortium, Esserman L, van' t Veer L, Beckwith H, Forero A, Rugo H. LIV-1 expression in primary breast cancers in the I-SPY 2 TRIAL [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-10-14.

Abstract P3-10-02: Identifying breast cancer molecular phenotypes to predict response in a modern treatment landscape: Lessons from ˜1000 patients across 10 arms of the I-SPY 2 TRIAL

Background: The explosion in new treatment options targeting immune checkpoints, HER signaling, DNA repair deficiency, AKT, and other pathways calls for updated breast cancer subtypes beyond HR and HER2 status to predict which patients will respond to which treatments. Here we leverage the I-SPY 2 TRIAL biomarker program over the past 8 years across 10 treatment arms to elucidate a minimal set of biomarkers that may improve response prediction in a modern treatment context, and to investigate which new patient phenotypes are identified by these response-predictive biomarkers.

Methods: 986 patients were considered in this analysis. Treatments included paclitaxel alone (or with trastuzumab (H) in HER2+) or combined with investigational agents: veliparib/carboplatin (VC); neratinib; MK2206; ganitumab; ganetespib; AMG386; TDM1/pertuzumab (P); H/P; and pembrolizumab (Pembro). 24 prospectively defined, mechanism-of-action and pathway-based expression and phospho-protein signatures/biomarkers assayed from pre-treatment biopsies were previously found to be predictive in a particular agent/arm in pre-specified analysis. Here we evaluate these biomarkers in all patients. We assessed association between each biomarker and response in the population as a whole and within each arm and HR/HER2 subtype using a logistic model. To identify optimal dichotomizing thresholds for select biomarkers, 2-fold cross-validation was repeated 500 times. Our analysis is exploratory and does not adjust for multiplicities.

Results: Our initial set of 24 predictive biomarkers reflects DNA repair deficiency (n=2), immune activation (n=7), ER signaling (n=2), HER2 signaling (n=4), proliferation (n=2), phospho-activation of AKT/mTOR (n=2), and ANG/TIE2 (n=1) pathways, among others. Biomarkers reflecting similar biology are correlated and cluster together. We make use of this correlation structure to reduce the dimensionality of the biomarker set to five predictive signals: proliferation, DNA repair deficiency (DRD), immune-engaged (Immune+), luminal/ER (lum), and HER2-activated. These biomarkers, when dichotomized, identify patient groups with differential predicted sensitivities to I-SPY 2 agents and are present at different proportions within receptor subtypes. For instance, in the HER2- subset, Immune+/DRD+ patients are predicted sensitive to both VC and Pembro, and account for 39% of TN, but only 12% of HR+HER2-. On the other end of the spectrum, only 17% of TN are Immune-/DRD-, compared to the majority (56%) of HR+HER2-. There are also subsets of patients positive for only one marker. For the HER2+ subset, 67% are HER2-activated+, and 25% lum+; of these HER2-activated+ patients are more likely to be Immune+ (44%), vs 23% in lum+. HER2-activated+/Immune+ patients have higher predicted sensitivity to HER2-targeted agents than lum+ or Immune- patients.

In all, these molecular phenotypes predict sensitivity to one or more I-SPY 2 investigational agents for 75% of the ˜ 1000 patients.

Conclusion: Molecular phenotypes reflecting proliferation, immune engagement, HER2-activation, luminal/ER-signaling, and DNA repair deficiency may provide a roadmap to guide treatment prioritization for emerging therapeutics.

Citation Format: Wolf DM, Yau C, Wulfkuhle J, Petricoin E, Campbell M, Brown-Swigart L, Hirst G, Asare S, Zhu Z, Lee EP, Delson A, Pohlmann P, I-SPY 2 TRIAL Consortium, Hylton N, Liu MC, Symmans F, DeMichele A, Yee D, Berry D, Esserman L, van 't Veer L. Identifying breast cancer molecular phenotypes to predict response in a modern treatment landscape: Lessons from ˜1000 patients across 10 arms of the I-SPY 2 TRIAL [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-10-02.

Abstract PD6-08: Analysis of immune infiltrates (assessed via multiplex fluorescence immunohistochemistry) and immune gene expression signatures as predictors of response to the checkpoint inhibitor pembrolizumab in the neoadjuvant I-SPY 2 trial

Background: Pembrolizumab (Pembro), an anti-PD-1 immune checkpoint inhibitor, has been approved for the treatment of a variety of cancers including melanoma, non-small cell lung cancer, head and neck squamous cell carcinoma, and urothelial carcinoma. Pembro was recently evaluated in HER2- breast cancer patients in the neoadjuvant I-SPY 2 TRIAL and graduated in the triple negative (TN), HR+HER2-, and HER2- signatures. HER2- patients were randomized to receive Pembro+paclitaxel followed by doxorubicin/cyclophosphamide (P+T -&gt; AC) vs. T -&gt; AC. We and others have shown that TN breast cancers tend to have high numbers of immune infiltrates, including T cells and tumor associated macrophages (TAMs). We evaluated expression signatures representing 14 immune cell types (TILs, T cells, CD8 T cells, exhausted T cells, Th1, Tregs, cytotoxic cells, NK, NK CD56dim, dendritic cells, mast cells, B cells, macrophages, and neutrophils) as specific predictors of response to Pembro.

Methods: Data from 248 patients (Pembro: 69; controls: 179) were available. Pre-treatment biopsies were assayed using Agilent gene expression arrays. Signature scores are calculated by averaging cell type specific genes. All I-SPY 2 qualifying biomarker analyses follow a pre-specified analysis plan. We used logistic modeling to assess biomarker performance. A biomarker is considered a specific predictor of Pembro response if it associates with response in the Pembro arm but not the control arm, and if the biomarker x treatment interaction is significant (likelihood ratio test, p&lt;0.05). This analysis is also performed adjusting for HR status as covariates, and within receptor subsets. For successful biomarkers, we use Bayesian modeling to estimate the pCR rates of 'predicted sensitive' patients in each arm. Our statistics are descriptive rather than inferential and do not adjust for multiplicities of other biomarkers outside this study.

Results: 10 out of the 14 cell-type signatures tested are associated with response in the Pembro arm. Higher expression levels of 9 of these cell-type signatures are associated with higher pCR rates (T cells, exhausted T cells, Th1, cytotoxic cells, NK, NK CD56dim, dendritic cells, B cells, and macrophages), whereas higher mast cell signature expression is associated with non-pCR. Interestingly, many of these same signatures also associate or trend towards association with response in the control arm; and in a model adjusting for HR status, only 3 of these signatures (Th1, B cells and dendritic cells) show significant interaction with treatment. Within the whole population and the TN subtype, the dendritic cell signature is the strongest predictor of specific response to Pembro (OR/1SD: 4.04 and 4.4, LR p &lt; 0.001 overall and in TN). Although other immune signatures (T cells, exhausted T cells, NK, and macrophages) also associate with response in the Pembro arm in the TN subtype, only the dendritic cell and Th1 signatures have a significant interaction with treatment. In contrast, in the HR+HER2- subtype, only 3 signatures (Th1, B cells, and mast cells) associate with response to Pembro; but none of these signatures have significant interaction with treatment. Of note, in both the Pembro and control arms, HR+HER2- patients with higher average mast cell marker expression have lower pCR rates (OR/1SD: 0.33 and 0.51, LRp: 0.006 and 0.04 in Pembro and control arm).

Conclusion: As expected, multiple immune cell expression signatures are predictive of response in the Pembro arm; but only dendritic cells and Th1 cells are specific to Pembro in both the population as a whole and the TN subtype. Interestingly, the presence of mast cells may impede response, especially in HR+HER2- patients. Correlation of these signatures with multiplex-IF immune markers is pending.

Citation Format: Campbell M, Yau C, Borowsky A, Vandenberg S, Wolf D, Rimm D, Nanda R, Liu M, Brown-Swigart L, Hirst G, Asare S, van't Veer L, Yee D, DeMichele A, Berry D, Esserman L. Analysis of immune infiltrates (assessed via multiplex fluorescence immunohistochemistry) and immune gene expression signatures as predictors of response to the checkpoint inhibitor pembrolizumab in the neoadjuvant I-SPY 2 trial [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr PD6-08.

Abstract PD6-14: Analysis of DNA repair deficiency biomarkers as predictors of response to the PD1 inhibitor pembrolizumab: Results from the neoadjuvant I-SPY 2 trial for stage II-III high-risk breast cancer

Background: Pembrolizumab (P), an anti-PD-1 immune checkpoint inhibitor, has been approved for treatment of microsatellite instability-high and mismatch repair deficient cancers. In I-SPY 2, patients were randomized to receive standard chemotherapy alone or in combination with an experimental agent. P was one of the experimental agents evaluated in HER2- patients in I-SPY 2 and graduated in the TN, HR+HER2-, and HER2- signatures. We hypothesize that a combination of two signatures predicting response to veliparib/carboplatin therapy in I-SPY 2 [MammaPrint High2 (MP2)/PARPi7-high] and reflecting DNA damage repair deficiency, may also predict response to P. In addition, we also tested 9 gene expression signatures reflecting different aspects of DNA damage and repair: FA, MMR, BER, HR, TLS, NER, NHEJ, DR, and DNA damage sensing (DDS) pathways.

Methods: Data from 249 patients (P: 69 and controls: 180) were available. Pre-treatment biopsies were assayed using Agilent gene expression arrays. All I-SPY 2 qualifying biomarker analyses follow a pre-specified analysis plan. We used logistic modeling to assess biomarker performance. A biomarker is considered a specific predictor of P response if it associates with response in the P arm but not the control arm, and if the biomarker x treatment interaction is significant (likelihood ratio test, p&lt;0.05). This analysis is also performed adjusting for HR status as a covariate, and within receptor subsets, sample size permitting. For successful biomarkers, we use Bayesian modeling to estimate the pCR rates of 'predicted sensitive' patients in each arm. Our statistics are descriptive rather than inferential and do not adjust for multiplicities of other biomarkers outside this study.

Results: MP2 status associates with pCR in P (OR=7.7; p=0.00021), but also to a lesser extent in the control arm (OR=2.4:p=0.045), with an OR ratio of 3.3 which trends toward significance, even after adjusting for HR status (LR p=0.083). A majority of TN patients are MP2; and TN/MP2 patients have an estimated pCR rate of 67% in P (vs. 23% in control). Although only ~30% of HR+HER2- patients were MP2, their estimated pCR rate in P is 61%, compared to 29% in unselected HR+/HER2- patients. PARPi7 predicted response in the P arm only in the HR+HER2- group (LR p= 0.025), but not in the population as a whole or the TN subtype. Combining MP2 and PARPi7 into MP2/PARPi7-high did not improve performance over MP2 as a single biomarker. Of the 9 DDR pathway signatures tested, both BER and DDS associate with pCR in P, but only DDS (which includes ATM, ATR, CHEK1-2) associates with pCR in the P arm (LR p=0.00029), and not the control arm (LR p=0.53), with a significant interaction with treatment (LR p=0.0064) that retains significance in a model adjusting for HR status. When dichotomized to optimize the biomarker x treatment interaction, the estimated pCR rate is 75% in P vs 18% in control, in the DDS+ subset.

Conclusion: In this small study, MP2 status and a DNA damage sensing pathway but not the PARPi7 or other repair pathways show promise as predictive biomarkers for immune checkpoint inhibition therapy in breast cancer.

Citation Format: Yau C, Wolf D, Brown-Swigart L, Hirst G, Sanil A, Singhrao R, I-SPY 2 TRIAL Investigators, Asare S, DeMichele A, Berry D, Esserman L, van 't Veer L, Nanda R, Liu M, Yee D. Analysis of DNA repair deficiency biomarkers as predictors of response to the PD1 inhibitor pembrolizumab: Results from the neoadjuvant I-SPY 2 trial for stage II-III high-risk breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr PD6-14.

Cyclic Stress Relaxation (Csr) of Filled Rubber and Rubber Components

Under repeated stressing it is well known that rubber materials exhibit cyclic stress relaxation (CSR). Previous work has shown that the amount of relaxation observed from cycle to cycle is significantly greater than that expected from static relaxation measurements. The reduction in the stress attained on the second and successive loading cycles as compared to the stress attained on the first cycle in a stress strain cyclic test of fixed amplitude has been measured for elastomer test pieces and engineering components. It is seen that the peak force, under cyclic testing to a specific maximum displacement, plotted against the number of cycles on logarithmic scales produces a straight line graph, whose slope correlates to the rate of cyclic stress relaxation per decade. The rate of cyclic stress relaxation was found to increase with displacement amplitude in all modes of deformation. Plotting the rate of stress relaxation per decade against the maximum average strain energy attained in the cycle reduces the data measured in different deformation modes for both simple test pieces and components to a single curve. This approach allows the cyclic stress relaxation in a real component to be predicted from simple laboratory tests.