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Background: The Phase III IMpassion130 study (NCT02425891) evaluated atezolizumab (anti–PD-L1) + nab-paclitaxel (nabPx) vs placebo + nabPx as first-line treatment for pts with metastatic triple-negative breast cancer (TNBC). The study met its co-primary PFS endpoint in ITT pts and in pts with PD-L1 ≥1% on tumor-infiltrating immune cells (IC+). Clinically meaningful OS benefit was seen at interim OS analysis, notably in pts with PD-L1 IC+ tumors (Table). Here we report exploratory efficacy data in immunologically and clinically relevant, biomarker-defined subgroups. Methods: Pts had histologically documented metastatic or unresectable locally advanced TNBC (evaluated locally per ASCO-CAP). Pts were randomized 1:1 to nabPx 100 mg/m2 IV (d1, 8 and 15 of a 28-d cycle) + atezolizumab 840 mg IV q2w or placebo (A-nabPx or P-nabPx) until progression or toxicity. Exploratory biomarkers were centrally analyzed: PD-L1 on tumor cells (TC) by VENTANA SP142 IHC assay, intratumoral CD8 by IHC, stromal tumor-infiltrating lymphocytes (sTILs), BRCA1/2 mutational status and ER/PR/HER2 status. Results: PD-L1 IC was highly predictive of A-nabPx efficacy (Table). The majority of PD-L1 TC+ tumors were also PD-L1 IC+. Intratumoral CD8, but not sTILs, were well correlated with PD-L1 IC. Consequently, CD8 was predictive of A-nabPx efficacy for PFS/OS, while sTILs only predicted PFS benefit. Local vs central TNBC assessment was concordant in most pts. Local vs central lab–defined TNBC populations derived similar benefit from A-nabPx. Efficacy by BRCA status will be presented to evaluate the benefits of immunotherapy for this subgroup. Conclusions: Exploratory efficacy analyses from IMpassion130 suggest consistency between local and central ER/PR/HER2 testing and that PD-L1 IC is the most robust predictive biomarker for selecting untreated mTNBC pts who benefit from A-nabPx. PopulationA-nabPxP-nabPxPrimary data, stratifiedITT, n451451mPFS (95% CI), mo7.2 (5.6-7.5)5.5 (5.3-5.6)PFS HR (95% CI)0.80 (0.69-0.92); P=0.0025mOS (95% CI), mo21.3 (17.3-23.4)17.6 (15.9-20.0)OS HR (95% CI)0.84 (0.69-1.02); P=0.0840PD-L1 IC+, n (%)185 (41%)184 (41%)mPFS (95% CI), mo7.5 (6.7-9.2)5.0 (3.8-5.6)PFS HR (95% CI)0.62 (0.49-0.78); P<0.0001mOS (95% CI) mo25.0 (22.6-NE)15.5 (13.1-19.4)OS HR (95% CI)0.62 (0.45-0.86)aExploratory/biomarker data, unstratifiedPD-L1 TC evaluable, n449451PD-L1 TC+, n (%)38 (8%)40 (9%)PFS HR (95% CI)0.51 (0.31-0.84)OS HR (95% CI)0.63 (0.33-1.21)CD8 evaluable, n371349CD8 ≥0.5%, n (%)261 (70%)239 (68%)PFS HR (95% CI)0.74 (0.61-0.91)OS HR (95% CI)0.66 (0.50-0.88)sTIL evaluable, n448444sTIL ≥10%, n (%)147 (33%)137 (31%)PFS HR (95% CI)0.66 (0.50-0.86)OS HR (95% CI)0.75 (0.51-1.10)cTNBC evaluable, n420412cTNBC ITT, n (%)307 (73%)317 (77%)PFS HR (95% CI)0.81 (0.68-0.98)OS HR (95% CI)0.85 (0.67-1.08)cTNBC PD-L1 IC+, n (%)133 (43%)134 (42%)PFS HR (95% CI)0.67 (0.51-0.88)OS HR (95% CI)0.69 (0.47-1.00)Data cutoff: 17 April 2018 (12.9-mo median follow up).cTNBC, centrally confirmed TNBCTC/IC+, PD-L1 ≥1% (VENTANA SP142 assay)a Not formally tested per hierarchical study design. Citation Format: Emens LA, Loi S, Rugo HS, Schneeweiss A, Diéras V, Iwata H, Barrios CH, Nechaeva M, Molinero L, Nguyen Duc A, Funke R, Chui SY, Husain A, Winer EP, Adams S, Schmid P. IMpassion130: Efficacy in immune biomarker subgroups from the global, randomized, double-blind, placebo-controlled, phase III study of atezolizumab + nab-paclitaxel in patients with treatment-naïve, locally advanced or metastatic triple-negative breast cancer [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 GS1-04.

In the context of software startups, project failure is embraced actively and considered crucial to obtain validated learning that can lead to pivots. A pivot is the strategic change of a business concept, product or the different elements of a business model. A better understanding is needed on different types of pivots and different factors that lead to failures and trigger pivots, for software entrepreneurial teams to make better decisions under chaotic and unpredictable environment. Due to the nascent nature of the topic, the existing research and knowledge on the pivots of software startups are very limited. In this study, we aimed at identifying the major types of pivots that software startups make during their startup processes, and highlighting the factors that fail software projects and trigger pivots. To achieve this, we conducted a case survey study based on the secondary data of the major pivots happened in 49 software startups. 10 pivot types and 14 triggering factors were identified. The findings show that customer need pivot is the most common among all pivot types. Together with customer segment pivot, they are common market related pivots. The major product related pivots are zoom-in and technology pivots. Several new pivot types were identified, including market zoom-in, complete and side project pivots.Our study also demonstrates that negative customer reaction and flawed business model are the most common factors that trigger pivots in software startups. Our study extends the research knowledge on software startup pivot types and pivot triggering factors. Meanwhile it provides practical knowledge to software startups, which they can utilize to guide their effective decisions on pivoting.pivots that software startups make when failures happen. To this end, the research questions that guided the study are phrased as following: RQ1: What are the factors that trigger software startups to pivot? RQ2: What are the types of pivots software startups undertake?To answer the research questions, we employed a systematic research process. We collected online materials as secondary data and analysed the major pivots in 49 software startups reported in these materials, including the well-known companies such as YouTube, Flickr, Pinterest and Twitter. The online materials allowed us to quickly obtain useful data on as many significant pivots in software startups as possible. Based on the analysis of the pivots in these 49 software startups, we extracted a list of factors that triggered them to pivot, and identified a set of major types of pivots they conducted. To better structure the triggering factors and pivot types, we categorized them into different groups respectively.The rest of this paper is organized as follows: in Section 2, the background literature and related work are reviewed. Section 3 describes the research approach employed in the study. The research findings are presented in detail in Section 4, and further discussed in Section 5. The paper is summarized in Section 6, which also outlines th...

Background In the Phase III IMpassion130 study, atezolizumab plus nab-paclitaxel (A+nP) showed clinical benefit in advanced/metastatic triple-negative breast cancer (TNBC) patients who were programmed death-ligand 1 (PD-L1) + (tumor-infiltrating immune cells [IC] ≥1%) using the SP142 immunohistochemistry (IHC) assay. Here we evaluate 2 other PD-L1 assays for analytical concordance with SP142 and patient-associated clinical outcomes. Methods Samples from 614 patients (68.1% of intention-to-treat population) were centrally evaluated by IHC for PD-L1 status on IC (VENTANA SP142, SP263, Dako 22C3) or as a combined positive score (CPS; 22C3). Results Using SP142, SP263, and 22C3 assays, PD-L1 IC ≥ 1% prevalence was 46.4% (95% confidence interval [CI] = 42.5–50.4%), 74.9% (95% CI = 71.5–78.3%), and 73.1% (95% CI = 69.6–76.6%), respectively; 80.9% were 22C3 at CPS ≥1. At IC ≥ 1% (+), the analytical concordance between SP142 and SP263 and 22C3 was 69.2% and 68.7%, respectively. Almost all SP142+ cases were captured by other assays (double positive), but several SP263 + (29.6%) or 22C3 + (29.0%) cases were SP142– (single positive). A+nP clinical activity vs placebo+nP in SP263+ and 22C3+ patients (progression-free survival [PFS] hazard ratios [HRs] = 0.64–0.68; overall survival [OS] HRs = 0.75–0.79) was driven by double-positive (PFS HRs = 0.60–0.61; OS HRs = 0.71–0.75) rather than single-positive cases (PFS HRs = 0.68–0.81; OS HRs = 0.87–0.95). Concordance for harmonized cutoffs for SP263 (IC ≥ 4%) and 22C3 (CPS ≥10) to SP142 IC ≥ 1% was subpar (approximately 75%). Conclusions 22C3 and SP263 assays identified more patients as PD-L1 + (IC ≥ 1%) than SP142. No inter-assay analytical equivalency was observed. Consistent improved A+nP efficacy was captured by the SP142 PD-L1 IC ≥ 1% subgroup nested within 22C3 and SP263 PD-L1 + (IC ≥ 1%) populations.

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Background: In the Phase 3 IMpassion130 (NCT02425891) trial in metastatic triple-negative breast cancer (mTNBC), first-line atezolizumab + nab-paclitaxel (A+nP) significantly improved PFS vs placebo + nab-paclitaxel (P+nP) in the intent-to-treat (ITT) and PD-L1+ (PD-L1-stained immune cells [IC] ≥ 1% of the tumor area by VENTANA PD-L1 SP142 assay) populations. SP142 is currently the only validated assay for selecting patients who may derive benefit with A+nP. In post-hoc analyses of IMpassion130 (Rugo, ESMO 2019, submitted), PD-L1 status was also evaluated by Dako 22C3 and VENTANA SP263 assays. The SP142+ population (IC ≥ 1%; 46% prevalence) was captured within the 22C3 (CPS ≥ 1, 81% prevalence) and SP263 (IC ≥ 1%, 75% prevalence) subgroups, which identified more patients (pts) with PD-L1+ tumors. A+nP clinical activity was highest in pts identified as PD-L1+ by both SP142 and 22C3/SP263. HRs for clinical activity were lower in pts identified as PD-L1+ by 22C3/SP163, but PD-L1- by SP142. There was no suggestion of clinical benefit in pts identified as PD-L1- by both SP142 and 22C3/SP263. In the current retrospective exploratory analysis, we attempted to harmonize the PD-L1 assays by identifying cutoffs for 22C3 or SP263 that replicate the SP142 IC ≥ 1% population. Methods: Samples from IMpassion130 were assessed by a central laboratory for PD-L1 expression using VENTANA SP142 or SP263 IHC assays or Dako 22C3 IHC assay. Optimal cutoffs for 22C3 and SP263 were identified as those that maximize the analytical concordance (defined as overall percentage agreement; OPA) with the clinically validated SP142 IC 1% cutoff as the reference standard. Association with clinical activity was analyzed in the biomarker-evaluable population (BEP) in tumor samples from pts evaluated by the three PD-L1 assays. Results: In the BEP (n = 614; 68% of ITT), the intraclass correlation index (Spearman r) between SP142 IC and 22C3 CPS or SP263 IC was 0.57 and 0.69, respectively. The model-derived cutoffs with highest OPA (75%) for SP142 IC ≥ 1% were CPS 10 for 22C3 and IC 4% for SP263. Compared with our previous analyses at standard cutoffs (22C3 CPS 1; SP263 IC 1%), model-derived cutoffs resulted in negative percentage agreement increases from 45% to 74% (22C3) and from 34% to 77% (SP263), accompanied by positive percentage agreement reductions from 98% to 74% (22C3) and to 73% (SP263). These data suggest that the SP142 assay may identify a different population from the 22C3 or SP263 assays. For 22C3 at CPS 10, 36% of pts were SP142+/22C3+, but 10% were SP142+/22C3-, and 17% SP142-/22C3+. For SP263 at IC 4%, 34% of pts were SP142+/SP263+, but 12% were SP142+/SP263-, and 12% SP142-/SP263+. See table for prevalences, medians and HR point estimates for PFS and OS with the model-derived cutoffs. Conclusions: The suboptimal OPAs achieved with the model-derived cutoffs indicate that the assays could not be harmonized. Differences in SP142+ vs 22C3+ or SP263+ populations at model-derived cutoffs suggest that SP142, 22C3 and SP263 may not identify the same tumor biology. Additional data are required to understand these differences. Findings from these hypothesis-generating, post-hoc exploratory analyses based on mathematical modeling should be interpreted with caution. Currently, the VENTANA PD-L1 SP142 IHC assay (IC ≥ 1%) is the only clinically validated companion assay to select pts with mTNBC for A+nP treatment. Populationn (%)Median PFS, moPFS HRMedian OS, moOS HRA+nPP+nP(95% CI)A+nPP+nP(95% CI)BEP614 (100)7.45.40.7221.119.20.84(0.61, 0.86)(0.68, 1.03)SP142IC ≥ 1%285 (46)8.34.10.627.317.90.74(0.47, 0.78)(0.54, 1.01)IC < 1%329 (54)5.75.60.8620.820.70.95(0.68, 1.09)(0.72, 1.27)22C3CPS ≥ 10325 (53)7.55.50.712218.70.77(0.56, 0.91)(0.57, 1.03)CPS < 10289 (47)5.85.40.7320.219.40.94(0.57, 0.93)(0.69, 1.26)SP263IC ≥ 4%286 (47)8.75.50.6428.919.60.71(0.49, 0.83)(0.51, 0.98) Citation Format: Hope Rugo, Sherene Loi, Sylvia Adams, Peter Schmid, Andreas Schneeweiss, Carlos H Barrios, Hiroji Iwata, Véronique Diéras, Eric P Winer, Mark M Kockx, Dieter Peeters, Stephen Y Chui, Jennifer C Lin, Anh Nguyen Duc, Guiseppe Viale, Luciana Molinero, Leisha A Emens. Exploratory analytical harmonization of PD-L1 immunohistochemistry assays in advanced triple-negative breast cancer: A retrospective substudy of IMpassion130 [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 PD1-07.

Abstract. Minimum viable product (MVP) is the main focus of both business and product development activities in software startups. We empirically explored five early stage software startups to understand how MVP are used in early stages. Data was collected from interviews, observation and documents. We looked at the MVP usage from two angles, software prototyping and boundary spanning theory. We found that roles of MVPs in startups were not fully aware by entrepreneurs. Besides supporting validated learning, MVPs are used to facilitate product design, to bridge communication gaps and to facilitate cost-effective product development activities. Entrepreneurs should consider a systematic approach to fully explore the value of MVP, as a multiple facet product (MFP). The work also implies several research directions about prototyping practices and patterns in software startups.