Predictive biomarkers for survival benefit with ramucirumab in urothelial cancer in the RANGE trial

Michiel S van der Heijden, Thomas Powles, Daniel Petrylak, Ronald de Wit, Andrea Necchi, Cora N Sternberg, Nobuaki Matsubara, Hiroyuki Nishiyama, Daniel Castellano, Syed A Hussain, Aristotelis Bamias, Georgios Gakis, Jae-Lyun Lee, Scott T Tagawa, Ulka Vaishampayan, Jeanny B Aragon-Ching, Bernie J Eigl, Rebecca R Hozak, Erik R Rasmussen, Meng Summer Xia, Ryan Rhodes, Sameera Wijayawardana, Katherine M Bell-McGuinn, Amit Aggarwal, Alexandra Drakaki, Michiel S van der Heijden, Thomas Powles, Daniel Petrylak, Ronald de Wit, Andrea Necchi, Cora N Sternberg, Nobuaki Matsubara, Hiroyuki Nishiyama, Daniel Castellano, Syed A Hussain, Aristotelis Bamias, Georgios Gakis, Jae-Lyun Lee, Scott T Tagawa, Ulka Vaishampayan, Jeanny B Aragon-Ching, Bernie J Eigl, Rebecca R Hozak, Erik R Rasmussen, Meng Summer Xia, Ryan Rhodes, Sameera Wijayawardana, Katherine M Bell-McGuinn, Amit Aggarwal, Alexandra Drakaki

Abstract

The RANGE study (NCT02426125) evaluated ramucirumab (an anti-VEGFR2 monoclonal antibody) in patients with platinum-refractory advanced urothelial carcinoma (UC). Here, we use programmed cell death-ligand 1 (PD-L1) immunohistochemistry (IHC) and transcriptome analysis to evaluate the association of immune and angiogenesis pathways, and molecular subtypes, with overall survival (OS) in UC. Higher PD-L1 IHC and immune pathway scores, but not angiogenesis scores, are associated with greater ramucirumab OS benefit. Additionally, Basal subtypes, which have higher PD-L1 IHC and immune/angiogenesis pathway scores, show greater ramucirumab OS benefit compared to Luminal subtypes, which have relatively lower scores. Multivariable analysis suggests patients from East Asia as having lower immune/angiogenesis signature scores, which correlates with decreased ramucirumab OS benefit. Our data highlight the utility of multiple biomarkers including PD-L1, molecular subtype, and immune phenotype in identifying patients with UC who might derive the greatest benefit from treatment with ramucirumab.

Conflict of interest statement

M.S.v.d.H. has received research support from Bristol Myers Squibb, AstraZeneca, Roche, and 4SC; and consultancy fees from BMS, Merck, Sharp & Dhome, Roche, AstraZeneca, Seattle Genetics, Janssen, and Pfizer (all paid to Institute). T.P. reports consulting honoraria from AstraZeneca, BMS, Exelixis, Incyte, Ipsen, Merck, MSD, Novartis, Pfizer, Seattle Genetics, Merck Serono, Astellas, Johnson & Johnson, Eisai, and Roche; grants/funding (to Institution) from AstraZeneca, BMS, Exelixis, Ipsen, Merck, MSD, Novartis, Pfizer, Seattle Genetics, Merck Serono, Astellas, Johnson & Johnson, Eisai, and Roche; and travel expenses from Roche, Pfizer, MSD, AstraZeneca, and Ipsen. D.P. has received consultancy fees from Ada Cap (Advanced Accelerator Applications) Amgen, Astellas, AstraZeneca, Bayer, Bicycle Therapeutics, Boehringer Ingelheim, Bristol Myers Squibb, Clovis Oncology, Eli Lilly and Company, Exelixis, Gilead Sciences, Incyte, Janssen, Mirati, Monopteros, Pfizer, Pharmacyclics, Regeneron, Roche, Seattle Genetics, and Urogen; grant support from Ada Cap (Advanced Accelerator Applications), Agensys Inc., *Astellas, AstraZeneca, *Bayer, BioXcel Therapeutics, Bristol Myers Squibb, Clovis Oncology, Eisai, *Eli Lilly and Company, *Endocyte, Genentech, *Innocrin, MedImmune, Medivation, Merck, Mirati, *Novartis, Pfizer, *Progenics, Replimune, Roche, *Sanofi Aventis, and Seattle Genetics (*denotes study trials have terminated); and ownership interest/investment in Bellicum (sold 7/2020) and Tyme (sold 10/2019). R.d.W. has received consultancy fees from Merck, Sanofi, Astellas, Janssen, Clovis, Orion, and Bayer; speaker fees from Astellas, Sanofi, and Merck; and has received research grants (to Institution) from Sanofi and Bayer. A.N. has a consulting role with Merck, AstraZeneca, Janssen, Incyte, Roche, Rainier Therapeutics, Clovis Oncology, Bayer, Astellas/Seattle Genetics, Ferring, and Immunomedics; has received grant/research support from Merck, Ipsen, and AstraZeneca; and travel expenses/honoraria from Roche, Merck, AstraZeneca, and Janssen. C.N.S. has served in an advisory/consultancy role for Pfizer, MSD, Merck, AstraZeneca, Astellas, Sanofi-Genzyme, Roche-Genentech, Incyte, Medscape, UroToday, and Foundation Medicine. N.M. reports personal fees from Chugai, Janssen, MSD, and Sanofi and grants from Astellas Pharma, Inc., AstraZeneca, Chugai, Eli Lilly and Company, Taiho, Janssen, MSD, Takeda, Amgen, and Pfizer. H.N. reports personal fees from Astellas Pharma, Inc., Chugai, Janssen, Merck, Sharp & Dhome, and Nippon Kayaku and grants from Astellas Pharma, Inc., Ono, and Chugai. S.A.H. has an advisory/consulting role with Roche, MSD, AstraZeneca, BMS, Janssen, Astellas, Bayer, Ipsen, Pfizer, Pierre Fabre, and Sotio and has received research funding from Cancer Research UK, MRC/NIHR, Janssen, and Boehringer Ingelheim. A.B. has an advisory/consulting role with Roche, Pfizer, Bristol Myers Squibb, AstraZeneca, and IPSEN Pharma; receiving honoraria from Bristol Myers Squibb, IPSEN Pharma, and Merck, Sharpe & Dohme and research support from AstraZeneca, Bristol Myers Squibb, Pfizer, IPSEN Steering Committee, and Roche. G.G. has served on an Advisory Board (last 5 years) for Bayer, MSD, Medac, IPSEN Pharma, Merck, LEO Pharma, and Astellas and has acted as an expert for Erbe Elektromedizin, IPSEN Pharma, Roche, Ferring, LEO Pharma, Merck, and Medac. S.T.T. reports research funding (to Institution) from Eli Lilly and Company and Sanofi and honoraria from Sanofi. U.V. reports research support from Astellas, Merck, and Bristol Myers Squibb and consulting and honoraria from AAA Pharmaceutical, Aveo, Bristol Myers Squibb, Bayer, Exelixis, Merck, Pfizer, and Sanofi. J.B.A.-C. serves on the Speakers’ Bureau of Astellas/Seattle Genetics and Bristol Myers Squibb and receives advisory board fees from EMD Serono, Pfizer, Aveo Pharmaceuticals, Immunomedics, AstraZeneca, Exelixis, and Merck. B.J.E. serves in an advisory/consultancy role for Merck, Janssen, AstraZeneca, Roche, Pfizer, EMD Serono, and SEAGEN. R.R.H., E.R.R., M.S.X., and S.W. are employees and shareholders of Eli Lilly and Company. K.M.B.-M. is a shareholder and former employee of Eli Lilly and Company and a shareholder and current employee of AbbVie. A.A. is a shareholder and former employee of Eli Lilly and Company and is currently an employee of Daiichi Sankyo, Inc. (US). A.D. has an advisory/consulting role with AstraZeneca, PACT Pharma, Astellas/Seattle Genetics, Janssen, Nektar, Bristol Myers Squibb, Radmetrix, Merck, Roche/Genentech, Exelixis, and Dyania Health; has received travel reimbursement from Eli Lilly and Company, AstraZeneca, and Seattle Genetics; holds stock and/or other interests in Attica Sciences and ATHOS Therapeutics; and has received research funding (to Institution unless noted otherwise) from AstraZeneca, Genentech/Roche, BMS, Merck Sharp & Dohme, Jounce Therapeutics, Infinity Pharmaceuticals, Seattle Genetics/Astellas, and Kite/Gilead (to A.D.). The disclosures reported herein, for each author, are potential conflicts only on the premise that the companies listed manufacture drugs for cancer treatment. The following authors declare no competing interests: D.C., A.B., J.L.L., R.R.

© 2022. The Author(s).

Figures

Fig. 1. Biomarker association with clinical outcome:…
Fig. 1. Biomarker association with clinical outcome: PD-L1 and signature pathways.
ac Kaplan–Meier curves depicting OS probability in ramucirumab + docetaxel or placebo + docetaxel arms based on PD-L1 expression. PD-L1 scoring method and cutoffs scored by a CPS < 10 (n = 126 participants) vs. ≥10 (n = 101 participants), b TC < 1 (n = 127 participants) vs. ≥1 (n = 100 participants), and c IC < 4 (n = 124 participants) vs. ≥4 (n = 103 participants). ORR, median OS, and stratified/unstratified HRs are shown. Stratification was based on geographical region, baseline ECOG PS, and visceral metastases. A two-sided Wald test was used in Cox regression models. p-values before BH-adjustment are shown in figures. p-values after BH-adjustment are shown in Supplementary Table 1. For all models, the TR1 population (n = 227 participants) is used, and the number within each subset is reported above. *Indicates proportional hazard assumption was violated with p = 0.04. Source data are provided as a Source Data file. BH, Benjamini-Hochberg; CI, confidence interval; CPS, combined positive score; ECOG PS, Eastern Cooperative Oncology Group Performance Status; HR, hazard ratio; IC, immune cell; n, number of participants; ORR, objective response rate; OS, overall survival; PD-L1, programmed cell death ligand 1; TC, tumor cell; TR, translational research.
Fig. 2. Molecular subtype association with PD-L1…
Fig. 2. Molecular subtype association with PD-L1 status, angiogenesis/immune signatures, and clinical outcome.
a Heatmap of angiogenesis and immune gene signatures in the TR2 population (n = 394 participants). Columns (patient samples) are ordered by CPS PD-L1 group (CPS not measured, CPS 0–9 and CPS 10–96) and by region (East Asia, Europe/Other, and North America). b Mean signature score for angiogenesis and immune pathways in CPS < 10 (n = 126) vs. CPS ≥ 10 (n = 101) samples from n = 227 participants in the TR1 population. For boxplots, the center line represents median, box hinges represent first and third quartiles, whiskers represent minimum and maximum within 1.5x interquartile range, and red marker is mean. Mean angiogenesis signature score, p = 0.009 (CPS < 10 vs. CPS ≥ 10, two-sample t-test without multiplicity adjustment). Mean immune signature score, p < 0.0001 (CPS < 10 vs. CPS ≥ 10, two-sample t-test without multiplicity adjustment). c Forest plot of stratified OS HRs (95% CIs) of ramucirumab + docetaxel vs. placebo + docetaxel for the subgroups defined by dichotomized individual angiogenesis and immune signatures, and corresponding mean of angiogenesis and immune signature score. Data are presented as estimated HR with error bars indicating the 95% CI. Each signature is dichotomized by the median. Citations for the angiogenesis signature pathways can be found in the Methods and Supplementary Table 2. Stratification was based on geographical region, baseline ECOG PS, and visceral metastases. The proportional hazard assumption was not violated in any instance. The TR2 population (n = 394 participants) was used; n = 197 participants within each subgroup. *Interaction p-value < 0.1 (0.055 for T-cell inflamed; 0.063 for mean immune); **Interaction p-value < 0.05 (0.036 for T-effector; 0.022 for activated CD4). p-values were based on two-sided Wald test without multiplicity adjustment. Source data are provided as a Source Data file. CI, confidence interval; CPS, combined positive score; CR, complete response; HR, hazard ratio; IC, immune cell; n, number of participants; NE, not evaluable; PD, progressive disease; PD-L1, programmed cell death ligand 1; PR, partial response; SD, stable disease; TC, tumor cell.
Fig. 3. Molecular subtype association with PD-L1…
Fig. 3. Molecular subtype association with PD-L1 CPS and mean angiogenesis/immune signature scores.
a Association of PD-L1 expression by CPS with molecular subtypes as defined by the Decipher GSCv1 and ConsensusMIBC classification schemes in the TR1 population (n = 227 samples from n = 227 participants in the TR1 population). For boxplots, the center line represents median, box hinges represent first and third quartiles, whiskers represent minimum and maximum within 1.5x interquartile range, and red marker is mean. Decipher GSCv1 F-test, p < 0.0001; ConsensusMIBC F-test, p < 0.0001. p-values indicated are for one-way ANOVA, without multiplicity adjustment. The blue dotted line indicates CPS ≥ 10 cutoff. Number and percent of patients with CPS ≥ 10 for each tumor subtype indicated below the plot. b Mean of angiogenesis signature scores relative to molecular subtypes of the Decipher GSCv1 and ConsensusMIBC classification schemes in the TR2 population (n = 394 samples from n = 394 participants in the TR2 population). Decipher GSCv1 F-test, p < 0.0001; ConsensusMIBC F-test, p < 0.0001, without multiplicity adjustment. p-values indicated are for one-way ANOVA. c Mean of immune signature scores relative to molecular subtypes of the Decipher GSCv1 and ConsensusMIBC classification schemes in the TR2 population (n = 394 samples from n = 394 participants in the TR2 population). Decipher GSCv1 F-test, p < 0.0001; ConsensusMIBC F-test, p < 0.0001. p-values indicated are for one-way ANOVA, without multiplicity adjustment. For boxplots, the center line represents median, box hinges represent first and third quartiles, whiskers represent minimum and maximum within 1.5x interquartile range, and red marker is mean. Decipher GSCv1 subtype prevalence (n/group): Luminal (n = 131), Luminal Infiltrated (n = 55), Basal (n = 150), Claudin Low (n = 58). ConsensusMIBC subtype prevalence (n/group): Luminal Papillary (n = 97), Luminal Non-Specified (n = 39), Luminal Unstable (n = 61), Stroma-rich (n = 75), Basal/Squamous (n = 114), Neuroendocrine-like (n = 8). Source data are provided as a Source Data file. Ba/Sq, Basal/Squamous; CPS, combined positive score; Lum NS, Luminal Non-Specified; Lum Pap, Luminal Papillary; Lum U, Luminal Unstable; NE-like, Neuroendocrine-like; PD-L1, programmed cell death ligand 1.
Fig. 4. Molecular subtype association with clinical…
Fig. 4. Molecular subtype association with clinical outcome.
Kaplan–Meier curves representing OS probability in ramucirumab + docetaxel or placebo + docetaxel arms based on Decipher GSCv1 molecular subtypes (Basal [n = 150 participants], Claudin Low [n = 58 participants], Luminal [n = 131 participants], and Luminal Infiltrated [n = 55 participants]) or ConsensusMIBC (Basal/Squamous [n = 114 participants], Stroma-rich [n = 75 participants], Luminal Non-Specified [n = 39 participants], Luminal Papillary [n = 97 participants], and Luminal Unstable [n = 61 participants]) molecular subtype. Neuroendocrine-like subtype was not analyzed due to low number of patient samples for this subtype (n = 8). ORR, median OS, and stratified/unstratified HRs are shown. A two-sided Wald test was used in Cox regression. p-values before BH-adjustment are shown in figures. p-values after BH-adjustment are shown in Supplementary Table 1. Stratification was based on geographical region, baseline ECOG PS, and visceral metastases. TR2 population (n = 394 participants) is used. The proportional hazard assumption was not violated in any instance. Source data are provided as a Source Data file. BH, Benjamini-Hochberg; CI, confidence interval; ECOG PS, Eastern Cooperative Oncology Group Performance Status; HR, hazard ratio; n, number of participants; ORR, objective response rate; OS, overall survival; TR, translational research.
Fig. 5. Forest plot of stratified HRs…
Fig. 5. Forest plot of stratified HRs (95% CIs) for overall survival based on inferred cell type from both molecular subtype classifications.
The ConsensusMIBC Neuroendocrine subtype had too few observations (n = 8 participants) for a stand-alone analysis and was not included. Data are presented as estimated HR with error bars indicating the 95% CI and box size indicating the sample size. The TR2 population (n = 394 participants) is used. Number of participants for each subgroup are: Basal + Basal Claudin Low, n = 208 participants; Luminal + Luminal Infiltrated, n = 186 participants; Basal/Squamous, n = 114 participants; Luminal*, n = 197 participants; and Stroma-rich, n = 75 participants. *“Luminal” grouping within the ConsensusMIBC classification includes Luminal Papillary, Luminal Unstable, and Luminal Non-Specified. Source data are provided as a Source Data file. CI, confidence interval; HR, hazard ratio; n, number of participants.
Fig. 6. Identification of clinical covariates associated…
Fig. 6. Identification of clinical covariates associated with angiogenesis- and immune- mean signature score and relation to clinical outcome.
a The coefficients estimated by a multivariable linear regression with indicated clinical covariates for the mean of angiogenesis or mean of immune signature score, respectively. Data are presented as mean estimated coefficient with error bars indicating the 95% CI. For categorical covariates with two levels (i.e., gender, region, histology, primary tumor site, visceral or liver metastases, and prior therapy), the coefficient represents the expectation of the mean signature score of the first category subtracted by the expectation of the second category as listed on the y-axis. For Bellmunt risk factor and age, the coefficient represents the slope of the mean signature score per 1-point increase in number of risk factors or 10-years age, respectively, as noted on the y-axis. Data are shown for n = 394 participants from the TR2 population. Significant associations are denoted by *, **, *** corresponding to *p = 0.01, **p = 0.005, ***p < 0.001 (t-test in linear regression without multiplicity adjustment; exact p-values are shown in Supplementary Table 5). b Mean angiogenesis and immune signature score in relation to geographic region (East Asia n = 74 vs. Other n = 320) in the TR2 population (n = 394 participants). Mean of angiogenesis signature score, p < 0.0001 (East Asia vs. Other, two-sample t-test, equal variance); mean of immune signature score, p < 0.001 (East Asia vs. Other, Welch two-sample t-test, unequal variance). For boxplots, the center line represents median, box hinges represent first and third quartiles, whiskers represent minimum and maximum within 1.5x interquartile range, and red marker is mean. c Proportion of molecular subtypes from the Decipher GSCv1 and ConsensusMIBC classification schemes across geographic region (East Asia n = 74 vs. Other n = 320). Pie charts show percentage of participants for indicated molecular subtype in the TR2 population (n = 394 participants). Source data are provided as a Source Data file. pt, point; yr, year.
Fig. 7. Molecular subtype association with mean…
Fig. 7. Molecular subtype association with mean angiogenesis/immune signature scores and geographical region.
a, b Mean of angiogenesis (a) and immune (b) signature scores, by geographic region, relative to molecular subtypes of the Decipher GSCv1 and ConsensusMIBC classification schemes in the TR2 population (n = 394 samples from n = 394 participants in the TR2 population). Decipher GSCv1 subtype prevalence (n/group; East Asia/Other): Luminal (n = 131; n = 30/n = 101), Luminal Infiltrated (n = 55; n = 6/n = 49), Basal (n = 150; n = 28/n = 122), Claudin Low (n = 58; n = 10/n = 48). ConsensusMIBC subtype prevalence (n/group; East Asia/Other): Luminal Papillary (n = 97; n = 24/n = 73), Luminal Non-Specified (n = 39, n = 4/n = 35), Luminal Unstable (n = 61; n = 7/n = 54), Stroma-rich (n = 75; n = 14/n = 61), Basal/Squamous (n = 114; n = 24/n = 90), Neuroendocrine-like (n = 8; n = 1/n = 7). For boxplots, center line represents median, box hinges represent first and third quartiles, whiskers represent minimum and maximum within 1.5x interquartile range, and red marker is mean. c Kaplan–Meier curves representing OS probability in ramucirumab + docetaxel or placebo + docetaxel arms based on geographic region. ORR, median OS, and stratified/unstratified HRs are shown. Two-sided Wald test was used in Cox regression. p-values before BH-adjustment are shown in figures. p-values after BH-adjustment are shown in Supplementary Table 1. Stratification was based on geographical region, baseline ECOG PS, and visceral metastases. TR2 population (n = 394 participants) is shown; there were n = 74 participants included in the East Asia subgroup and n = 320 participants included in the other regions subgroup. The proportional hazard assumption was not violated in any instance. Source data are provided as a Source Data file.BH, Benjamini-Hochberg; Ba/Sq, Basal/Squamous; CI, confidence interval; ECOG PS, Eastern Cooperative Oncology Group Performance Status; HR, hazard ratio; Lum NS, Luminal Non-Specified; Lum Pap, Luminal Papillary; Lum U, Luminal Unstable; n, number of participants; NE-like, Neuroendocrine-like; ORR, objective response rate; OS, overall survival; TR, translational research.

References

    1. Apolo AB, et al. Avelumab, an anti-programmed death-ligand 1 antibody, in patients with refractory metastatic urothelial carcinoma: results from a multicenter, phase Ib study. J. Clin. Oncol. 2017;35:2117–2124.
    1. Bellmunt J, et al. Pembrolizumab as second-line therapy for advanced urothelial carcinoma. N. Engl. J. Med. 2017;376:1015–1026.
    1. Fradet Y, et al. Randomized phase III KEYNOTE-045 trial of pembrolizumab versus paclitaxel, docetaxel, or vinflunine in recurrent advanced urothelial cancer: results of >2 years of follow-up. Ann. Oncol. 2019;30:970–976.
    1. Powles T, et al. Efficacy and safety of durvalumab in locally advanced or metastatic urothelial carcinoma: updated results from a phase 1/2 open-label study. JAMA Oncol. 2017;3:e172411.
    1. Powles T, et al. Avelumab maintenance therapy for advanced or metastatic urothelial carcinoma. N. Engl. J. Med. 2020;383:1218–1230.
    1. Rosenberg JE, et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet. 2016;387:1909–1920.
    1. Sharma P, et al. Nivolumab in metastatic urothelial carcinoma after platinum therapy (CheckMate 275): a multicentre, single-arm, phase 2 trial. Lancet Oncol. 2017;18:312–322.
    1. Iyer G, Rosenberg JE. Novel therapies in urothelial carcinoma: a biomarker-driven approach. Ann. Oncol. 2018;29:2302–2312.
    1. Powles T, Walker J, Andrew Williams J, Bellmunt J. The evolving role of PD-L1 testing in patients with metastatic urothelial carcinoma. Cancer Treat. Rev. 2020;82:101925.
    1. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of urothelial bladder carcinoma. Nature. 2014;507:315–322.
    1. Kamoun A, et al. A consensus molecular classification of muscle-invasive bladder cancer. Eur. Urol. 2020;77:420–433.
    1. Sjodahl G, et al. A molecular taxonomy for urothelial carcinoma. Clin. Cancer Res. 2012;18:3377–3386.
    1. Choi W, et al. Identification of distinct basal and luminal subtypes of muscle-invasive bladder cancer with different sensitivities to frontline chemotherapy. Cancer Cell. 2014;25:152–165.
    1. Kardos J, et al. Claudin-low bladder tumors are immune infiltrated and actively immune suppressed. JCI Insight. 2016;1:e85902.
    1. Robertson AG, et al. Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell. 2017;171:540–556.e25.
    1. Seiler R, et al. Impact of molecular subtypes in muscle-invasive bladder cancer on predicting response and survival after neoadjuvant chemotherapy. Eur. Urol. 2017;72:544–554.
    1. Powles T, et al. Atezolizumab versus chemotherapy in patients with platinum-treated locally advanced or metastatic urothelial carcinoma (IMvigor211): a multicentre, open-label, phase 3 randomised controlled trial. Lancet. 2018;391:748–757.
    1. Petrylak DP, et al. Ramucirumab plus docetaxel versus placebo plus docetaxel in patients with locally advanced or metastatic urothelial carcinoma after platinum-based therapy (RANGE): a randomised, double-blind, phase 3 trial. Lancet. 2017;390:2266–2277.
    1. Petrylak DP, et al. Ramucirumab plus docetaxel versus placebo plus docetaxel in patients with locally advanced or metastatic urothelial carcinoma after platinum-based therapy (RANGE): overall survival and updated results of a randomised, double-blind, phase 3 trial. Lancet Oncol. 2020;21:105–120.
    1. Agilent Dako. PD-L1 IHC 22C3 pharmDx Interpretation Manual–Urothelial Carcinoma (Agilent Dako, 2018).
    1. Necchi A, et al. Impact of molecular subtyping and immune infiltration on pathological response and outcome following neoadjuvant pembrolizumab in muscle-invasive bladder cancer. Eur. Urol. 2020;77:701–710.
    1. Zajac M, et al. Concordance among four commercially available, validated programmed cell death ligand-1 assays in urothelial carcinoma. Diagn. Pathol. 2019;14:99.
    1. McDermott DF, et al. Clinical activity and molecular correlates of response to atezolizumab alone or in combination with bevacizumab versus sunitinib in renal cell carcinoma. Nat. Med. 2018;24:749–757.
    1. Motzer RJ, et al. Molecular subsets in renal cancer determine outcome to checkpoint and angiogenesis blockade. Cancer Cell. 2020;38:803–817.
    1. Robinson BD, et al. Upper tract urothelial carcinoma has a luminal-papillary T-cell depleted contexture and activated FGFR3 signaling. Nat. Commun. 2019;10:2977.
    1. Loriot Y, et al. Erdafitinib in locally advanced or metastatic urothelial carcinoma. N. Engl. J. Med. 2019;381:338–348.
    1. Narayanan S, Srinivas S. Incorporating VEGF-targeted therapy in advanced urothelial cancer. Ther. Adv. Med. Oncol. 2017;9:33–45.
    1. Rosenberg JE, et al. Randomized phase III trial of gemcitabine and cisplatin with bevacizumab or placebo in patients with advanced urothelial carcinoma: results of CALGB 90601 (Alliance) J. Clin. Oncol. 2021;39:2486–2496.
    1. Loriot Y, et al. Phase 3 LEAP-011 trial: first-line pembrolizumab with lenvatinib in patients with advanced urothelial carcinoma ineligible to receive platinum-based chemotherapy [Abstract] Ann. Oncol. 2019;30:v356–v402.
    1. Bonetti M, Gelber RD. A graphical method to assess treatment-covariate interactions using the Cox model on subsets of the data. Stat. Med. 2000;19:2595–2609.
    1. Agilent Dako. PD-L1 IHC 22C3 pharmDx Interpretation Manual–Non-small Cell Lung Cancer (NSCLC) (Agilent Dako, 2019).
    1. Roche/Ventana. VENTANA PD-L1 (SP142) Assay—Interpretation Guide for Urothelial Carcinoma (Roche/Ventana, 2016).
    1. Piccolo SR, et al. A single-sample microarray normalization method to facilitate personalized-medicine workflows. Genomics. 2012;100:337–344.
    1. Liberzon A, et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015;1:417–425.
    1. Brauer MJ, et al. Identification and analysis of in vivo VEGF downstream markers link VEGF pathway activity with efficacy of anti-VEGF therapies. Clin. Cancer Res. 2013;19:3681–3692.
    1. Masiero M, et al. A core human primary tumor angiogenesis signature identifies the endothelial orphan receptor ELTD1 as a key regulator of angiogenesis. Cancer Cell. 2013;24:229–241.
    1. Uhlik MT, et al. Stromal-based signatures for the classification of gastric cancer. Cancer Res. 2016;76:2573–2586.
    1. Ayers M, et al. IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 2017;127:2930–2940.
    1. Herbst RS, et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature. 2014;515:563–567.
    1. Kowanetz M, et al. Differential regulation of PD-L1 expression by immune and tumor cells in NSCLC and the response to treatment with atezolizumab (anti-PD-L1) Proc. Natl Acad. Sci. USA. 2018;115:E10119–E10126.
    1. Charoentong P, et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017;18:248–262.
    1. de Jong JJ, et al. Long non-coding RNAs identify a subset of luminal muscle-invasive bladder cancer patients with favorable prognosis. Genome Med. 2019;11:60.
    1. Damrauer JS, et al. Intrinsic subtypes of high-grade bladder cancer reflect the hallmarks of breast cancer biology. Proc. Natl Acad. Sci. USA. 2014;111:3110–3115.
    1. Mo Q, et al. Prognostic power of a tumor differentiation gene signature for bladder urothelial carcinomas. J. Natl Cancer Inst. 2018;110:448–459.
    1. Marzouka NA, et al. A validation and extended description of the Lund taxonomy for urothelial carcinoma using the TCGA cohort. Sci. Rep. 2018;8:3737.
    1. Rebouissou S, et al. EGFR as a potential therapeutic target for a subset of muscle-invasive bladder cancers presenting a basal-like phenotype. Sci. Transl. Med. 2014;6:244ra291.
    1. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32:2847–2849.
    1. Grambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81:515–526.
    1. Eisenhauer EA, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1) Eur. J. Cancer. 2009;45:228–247.
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 1995;57:289–300.
    1. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acid Res. 2002;30:207–210.

Source: PubMed

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