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. 2023 Mar 31:15:17588359231156382.
doi: 10.1177/17588359231156382. eCollection 2023.

A transcriptomics approach to expand therapeutic options and optimize clinical trials in oncology

Vladimir Lazar  1 , Baolin Zhang  2 , Shai Magidi  3 , Christophe Le Tourneau  4 , Eric Raymond  5 , Michel Ducreux  6 , Catherine Bresson  3 , Jacques Raynaud  3 , Fanny Wunder  3 , Amir Onn  7 , Enriqueta Felip  8 , Josep Tabernero  8 , Gerald Batist  9 , Razelle Kurzrock  3 , Eitan Rubin  10 , Richard L Schilsky  3
Affiliations

A transcriptomics approach to expand therapeutic options and optimize clinical trials in oncology

Vladimir Lazar et al. Ther Adv Med Oncol. .

Abstract

Background: The current model of clinical drug development in oncology displays major limitations due to a high attrition rate in patient enrollment in early phase trials and a high failure rate of drugs in phase III studies.

Objective: Integrating transcriptomics for selection of patients has the potential to achieve enhanced speed and efficacy of precision oncology trials for any targeted therapies or immunotherapies.

Methods: Relative gene expression level in the metastasis and normal organ-matched tissues from the WINTHER database was used to estimate in silico the potential clinical benefit of specific treatments in a variety of metastatic solid tumors.

Results: As example, high mRNA expression in tumor tissue compared to analogous normal tissue of c-MET and its ligand HGF correlated in silico with shorter overall survival (OS; p < 0.0001) and may constitute an independent prognostic marker for outcome of patients with metastatic solid tumors, suggesting a strategy to identify patients most likely to benefit from MET-targeted treatments. The prognostic value of gene expression of several immune therapy targets (PD-L1, CTLA4, TIM3, TIGIT, LAG3, TLR4) was investigated in non-small-cell lung cancers and colorectal cancers (CRCs) and may be useful to optimize the development of their inhibitors, and opening new avenues such as use of anti-TLR4 in treatment of patients with metastatic CRC.

Conclusion: This in silico approach is expected to dramatically decrease the attrition of patient enrollment and to simultaneously increase the speed and detection of early signs of efficacy. The model may significantly contribute to lower toxicities. Altogether, our model aims to overcome the limits of current approaches.

Keywords: analogous normal tissue biopsies; clinical trials; oncology; transcriptomics; tumor biopsies.

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Conflict of interest statement

− Dr. Vladimir Lazar, Catherine Bresson, and Fanny Wunder are full-time employees of Worldwide Innovative Network (WIN) Association – WIN Consortium. − Shai Magidi receives consultancy fees from Worldwide Innovative Network (WIN) Association – WIN Consortium. − Worldwide Innovative Network (WIN) Association – WIN Consortium is the owner of the patent family entitled Digital Display. The inventors are Dr. Vladimir Lazar and Shai Magidi. − Dr. Baolin Zhang disclaimer: The views expressed in this manuscript are those of the author and do not represent the views or policies of the U.S. FDA. − Pr. Christophe Le Tourneau has participated in advisory boards from Celgene, AstraZeneca, MSD, BMS, Merck Serono, Nanobiotix, Rakuten, Seatlle Genetics, and Roche. − Pr. Eric Raymond : SCOR Health Science – consulting and shareholder; GenoScience Pharma – consulting and shareholder, Axoltis – shareholder, Stromacare – consulting and shareholderInstitut des Vaisseaux et du Sang (IVS) – Scientific Director, Institut National du Cancer (INCa France) – Member of the Board of Directors. − Pr. Michel Ducreux reports consulting or advisory role: Amgen, AstraZeneca, Bayer, Daiichi Sankyo, Merck Serono, MSD, Pierre Fabre, Roche, Servier. Speakers’ bureau: AstraZeneca, Bayer, Roche, Terumo Amir Onn receives consulting fees from: Roche Israel, MSD Israel, Boehringer Ingelheim, and AstraZeneca. − Enriqueta Felip reports consulting or advisory role: Abbvie, Amgen, AstraZeneca, Bayer, Blue Print Medicines, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lilly, GSK, Janssen, Merck, MSD, Novartis, Pfizer, Puma Biotechnology, Roche, Sanofi Genzyme, Takeda; Speakers’ bureau or expert testimony: AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lilly, Medscape, MSD, Novartis, Peervoice, Pfizer, Prime Oncology, Roche, Springer, Takeda, Touchime, CME Outfitters; Research grant or funding: Grant for Oncology Innovation (GOI), Fundación Merck Salud; Other: Grífols (independent member of the board). − Dr. Josep Tabernero declares scientific consultancy role for Array Biopharma, AstraZeneca, Bayer, BeiGene, Boehringer Ingelheim, Chugai, Genentech, Inc., Genmab A/S, Halozyme, Imugene Limited, Inflection Biosciences Limited, Ipsen, Kura Oncology, Lilly, MSD, Menarini, Merck Serono, Merrimack, Merus, Molecular Partners, Novartis, Peptomyc, Pfizer, Pharmacyclics, ProteoDesign SL, Rafael Pharmaceuticals, F. Hoffmann-La Roche Ltd, Sanofi, SeaGen, Seattle Genetics, Servier, Symphogen, Taiho, VCN Biosciences, Biocartis, Foundation Medicine, HalioDX SAS and Roche Diagnostics. − Gerald Batist collaborates in formal clinical trial contracts, IITs and in joint grants funded by Canadian and Quebec governments with Roche, Merck, Novartis, AstraZeneca, Bayer, Esperas, Aurka, Caprion, MRM. − Razelle Kurzrock is Chief Medical Officer of the Worldwide Innovative Network (WIN) Association – WIN Consortium. She has received research funding from Genentech, Merck Serono, Pfizer, Boehringer Ingelheim, TopAlliance, Takeda, Incyte, Debiopharm, Medimmune, Sequenom, Foundation Medicine, Konica Minolta, Grifols, Omniseq, and Guardant, as well as consultant and/or speaker fees and/or advisory board for X-Biotech, Neomed, Pfizer, Actuate Therapeutics, Roche, Turning Point Therapeutics, TD2/Volastra, Bicara Therapeutics, Inc., has an equity interest in IDbyDNA and CureMatch Inc, serves on the Board of CureMatch and CureMetrix, and is a co-founder of CureMatch. − Richard L. Schilsky is the Chairman of Worldwide Innovative Network (WIN) Association – WIN Consortium. He declares Research Support: continues to serve as the PI of the ASCO TAPUR trial. ASCO receives research grants from the following companies in support of this trial: AstraZeneca, Bayer, Boehringer-Ingelheim, Bristol Myers Squibb, Genentech, Lilly, Merck, Pfizer, Seagen. He does not personally receive any compensation from these companies; receives Consulting: he is consultant to the following companies: Brylogyx, Cellworks*, Clarified Precision Medicine*, EQRx*, Illumina*, Scandion Oncology* and receive compensation from those designated (*). − Dr. Baolin Zhang, Dr. Jacques Raynaud and Pr. Eitan Rubin, declare no potential conflicts of interest.

Figures

Figure 1.
Figure 1.
Differential expression (tumor versus normal tissues) and expression in tumor alone to assess prognostic value of MET and HGF. (a) KM OS survival probability of WINTHER patients with high and low differential expression of both MET and HGF based on the tumor and normal tissues: low expression level of both MET and HGF (n = 27, in blue color) with median survival of 18 months; high expression level of MET and/or HGF (n = 73, black color) with median survival of 4.7 months. X-axis: number of patients who were not censored in each time point. The log-rank p value < 0.0001. (b) KM OS survival curve of WINTHER patients with high and low expression of both MET and HGF based on the tumor expression only; low expression level (n = 14, blue color) with median survival of 15 months; high expression level of MET and/or HGF (n = 86, black color) with median survival of 5.7 months. The log-rank p value = 0.11. (c) Forest plot of HR based on a multivariate Cox proportional hazards model based on the tumor and normal tissues. The dotted line displayed at a HR of 1 indicates the HRs of the reference group. Hazard values are with 95% CI. (d) KM of OS survival probability in patients with high and low MET and HGF expression levels explored only in the tumor tissue biopsy in independent RNA-seq dataset of pancreatic ductal adenocarcinoma tumors (n = 177). (e) Independent RNA-seq dataset of HN squamous carcinoma tumors (n = 500). (f) RFS in an independent chip-seq dataset of breast cancers (n = 507). Validation datasets d, e, f available from the KMplotter tool. CI, confidence interval; HN, head and neck; HR, hazard ratio; KM, Kaplan–Meier; OS, overall survival; RFS, relapse-free survival.
Figure 2.
Figure 2.
Determining the eligible patient population that could benefit from treatment with c-MET inhibitors using the DDPP predictor of PFS. (a) Pearson correlation plot of the six-gene predictor with the PFS of three patients treated with c-MET inhibitors. Y-axis: median value of log2-based fold-changes tumor versus normal multiplied by log1.1 intensity of expression in the tumor for each of the six genes selected; X-axis: PFS in months. (b) Criteria of eligibility. Y-axis: log2 of the fold change tumor versus normal multiplied by the intensity of expression in tumor for MET (blue triangles) and HGF (red dots); X-axis: example of the subcohort of 17 patients with metastatic NSCLC, ordered in increasing expression of MET. Non-logged fold change is shown: blue circles high MET, red circles high HGF, and dotted circles both high MET and high HGF. DDPP, Digital Display Precision Predictor; PFS, progression-free survival.
Figure 3.
Figure 3.
(a) Proposed design of a study to confirm predicted efficacy of c-MET inhibitors. (b) All patients enrolled in stage I will be followed for at least 6 months and assessed at months 2, 4, 6 following c-MET inhibitor treatment initiation. If at least one patient in a cohort is observed to have CR, PR, or SD of at least 6 months duration according to RECIST v. 1.1, the cohort will be expanded in stage II to reach a total of 17 patients. If three or more patients out of the 17 are observed to have CR, PR, or SD of at least 6-month duration, the cohort will be declared to have a signal of drug activity. This design enables detection of signs of efficacy, rejecting null hypothesis (ORR < 5%) and retain the alternative hypothesis (ORR > 25%) with 80% power and a one-side type I error rate of 0.05. 36 patients are planned to be treated in the stage I and a maximum of 32 patients in the stage II, bringing a maximum number of patients treated to a total of 68, to assess which of the subcohorts are worth pursuing in phase III. CR, complete response; ORR, overall response rate; PR, partial response; SD, stable disease.
Figure 4.
Figure 4.
Prognostic value of immune check point targets. Forest plot of HRs of PFS based on a multivariate Cox proportional hazards model based of relative gene expression in tumor and normal tissues. The dotted line displayed at a HR of 1 indicates the HRs of the reference group. Hazard values are with 95% CI. (a) Evaluation of prognostic value of IO targets in metastatic CRC. (b) Evaluation of prognostic value of IO targets in metastatic NSCLC. CI, confidence interval; CRC, colorectal cancer; HR, hazard ratio; NSCLC, non-small-cell lung cancer; PFS, progression-free survival.
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