Therapeutic Advances in Medical Oncology: A transcriptomics approach to expand therapeutic options and optimize clinical trials in oncology
March 31, 2023
Published by Therapeutic Advances in Medical Oncology
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.
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Transcriptomics Plus 'Immune Tolerance' of Normal Tissue Shows Promise in Lung Cancer Prognosis
September 21, 2022
Precision Oncology News - Caroline Hopkins
NEW YORK – Researchers involved in the Worldwide Innovative Networking
(WIN) Consortium have shown, for the first time, that biomarkers detected in
normal lung tissue can be valuable for predicting lung cancer patients'
likelihood of post-surgery recurrence.
With several exceptions — namely germline testing — most biomarker insights in oncology rely on analysis of tissue or cells from the cancerous tumor itself. This is especially true in lung cancer, where somatic mutations or expression of genes in tumor tissue are routinely used to guide treatment decisions. Even most blood-based biomarker tests are tumor-centric, focusing on pieces of DNA shed from the tumor tissue circulating in blood.
Now, for the first time, researchers have demonstrated that the normal, healthy tissue in a patient's lung — that is, non-tumor lung tissue — can be rich in biomarkers that shed light on a patient's likelihood of relapse after surgery, information that oncologists can use to guide adjuvant therapy choices.
In a paper published last week in JCO Precision Oncology, WIN researchers, including Vladimir Lazar, the consortium's founder and chief scientific and operating officer, demonstrated that the so-called “immune tolerance profile” of normal lung tissue, when paired with a tumor-normal transcriptomics-based score, may allow oncologists better insights into patients' prognoses and the treatments most likely to benefit them.
“The problem is that today, there is no biomarker for predicting recurrence,” Lazar said. “The recurrence risk, today, is guided by stage and histology.”
In the retrospective analysis described in the JCO Precision Oncology paper, Lazar and colleagues analyzed data from the observational CHEMORES study, which followed 123 early-stage non-small cell lung cancer patients who
underwent surgical resection. Following surgery, based on their treating oncologists' decisions, 61 of these patients received adjuvant chemotherapy, while the other 62 did not. The observational study followed these patients for 92 months.
Researchers in the CHEMORES study performed DNA sequencing and gene expression analysis of resected tumor and normal lung tissue samples obtained during the patients' surgeries. These data were available to WIN researchers who aimed to test their hypothesis in silico that the differential gene expression between the tumor and normal lung tissues, together with the immune competent versus immune tolerant status of the normal lung tissue, could explain patients' risk of recurrence.
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NPJ Precision Oncology, a Nature Research journal
We are pleased to announce that WIN's article “Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival” has been published in NPJ Precision Oncology, a Nature Research journal.
Abstract
April 28, 2021
NPJ Precision Oncology, a Nature Research journal
The expanding targeted therapy landscape requires combinatorial biomarkers for patient stratification and treatment selection. This requires simultaneous exploration of multiple genes of relevant networks to account for the complexity of mechanisms that govern drug sensitivity and predict clinical outcomes. We present the algorithm, Digital Display Precision Predictor (DDPP), aiming to identify transcriptomic predictors of treatment outcome. For example, 17 and 13 key genes were derived from the literature by their association with MTOR and angiogenesis pathways, respectively, and their expression in tumor versus normal tissues was associated with the progression-free survival (PFS) of patients treated with everolimus or axitinib (respectively) using DDPP. A specific eight-gene set best correlated with PFS in six patients treated with everolimus: AKT2, TSC1, FKB-12, TSC2, RPTOR, RHEB, PIK3CA, and PIK3CB (r = 0.99, p = 5.67E−05). A two-gene set best correlated with PFS in five patients treated with axitinib: KIT and KITLG (r = 0.99, p = 4.68E−04). Leave-one-out experiments demonstrated significant concordance between observed and DDPP predicted PFS (r = 0.9, p = 0.015) for patients treated with everolimus. Notwithstanding the small cohort and pending further prospective validation, the prototype of DDPP offers the potential to transform patients’ treatment selection with a tumor- and treatment-agnostic predictor of outcomes (duration of PFS).
npj Precision Oncology (2021)5:33;
https://doi.org/10.1038/s41698-021-00171-6
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Nature Portfolio Cancer Community: Behind the Paper: Digital Display Precision Predictor: the prototype of a global biomarker model
Published Apr 29, 2021
Nature Portfolio Cancer Community
Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival
Digital Display Precision Predictor (DDPP) is potentially a new global biomarker model that can apply to any type of drug alone or in combination, agnostic of tumor type, and can lead, pending further prospective validation, to a new approach to optimal treatment selection for patients with cancer.
In my daily practice performing clinical tests using a genomics platform, I was often surprised by the discrepancy between laboratory test predictions and clinical reality.
A major question that remains unsatisfactorily answered for the treating physician is: what is the best drug for my patient? The current companion diagnostic approach used for most targeted therapies provides limited answers, with a binary “yes”/“no” expected response to a drug. But it does not provide any element of comparison with other drugs. In addition, with the number of validated drug targets increasing, testing each patient’s tumor for all markers related to all possible targeted therapies becomes infeasible due to the limited amount of tissue usually available from needle biopsies.
We therefore embarked on our research journey seeking a universal biomarker that could enable a rational choice of therapy among a large number of drugs and that would also address the complexity of cancer biology requiring the investigation of networks of pathways to understand the variability of clinical outcomes observed. Our objective was to create a biomarker tool able to predict in a single assay the time until tumor progression or death (progression-free survival (PFS)) to multiple targeted treatments: the Digital Display Precision Predictor (DDPP).
Our approach was based on the use of transcriptomics that our group, the Worldwide Innovative Network (WIN) Consortium for personalized cancer therapy, had explored successfully in the WINTHER clinical trial where the number of advanced cancer patients treated with a personalized targeted treatment was bolstered by 30% compared to using genomics alone to identify treatment options. WINTHER was the first trial that introduced transcriptomic investigations from biopsies of fresh frozen (FF) tumor and analogous normal tissues in order to significantly reduce the transcriptomic noise. The hypothesis was that apparent overexpression of genes in tumor cells could be driven by the normal tissue from which the tumor originated (reflecting variability between individuals and not carcinogenesis). The WINTHER dataset provided an incredible amount of information: genomics, whole transcriptomics (20,000 genes), treatment and clinical outcomes (progression-free survival (PFS) and overall survival). Key success factors for the transcriptomic data analysis in WINTHER were the standard operating procedures, operator training, and stringent histologic quality control of fresh frozen tissues (with high tumor content) obtained by biopsy.
The main hurdle that needed to be overcome in developing the DDPP was the small size of the WINTHER dataset and the variety of treatments employed that resulted in a small number of patients treated with the same drug (< 10) across several tumor types thus limiting the internal validation of our findings.
Unfortunately, our search for other comparable datasets was unsuccessful. No dataset had available for each patient: paired fresh frozen (FF), that is, tumor tissue (for ex NSCLC) and matching analogous normal tissue (bronchial mucosa in this case), transcriptomic data and clinical outcome.
The unavailability of another comparable independent dataset to further test our algorithm’s prediction of PFS under a variety of treatment options obliged us to be limited to one data source: WINTHER data. As a result, the conventional correlative methods such as Cox univariate and multivariate regression models, LASSO regression, multiple linear regression, failed. We went back to the drawing board.
We explored all the transcriptome results from WINTHER data for the patients who had received the same treatment looking for the group of genes which intersection's between differential expression (between tumor and normal tissue) and PFS would be spatially aligned on a Pearson Correlation. Inspiring ourselves from Euclid postulates, we concluded that if this was possible for a minimum of 3 patients, then the PFS was a linear function of the gene expression, and PFS could therefore be predicted for other patients.
We explored the DDPP ability to predict PFS by assessing correlations with PFS associated with everolimus, axitinib, trametinib, afatinib, experimental FGFR inhibitors and anti-PD-1/PDL-1 therapies received by WINTHER trial patients.
Based on our analysis, we confirmed the following:
(1) when using only tumor tissue, significance of the correlations dropped, supporting the importance of the tumor/normal analysis;
(2) the predictive value versus prognostic value of DDPP, by cross-correlating combined differential expression of the selected genes with PFS of patients under another treatment;
(3) ability of analyses to be used as predictors and address potential over-fitting, by performing leave-one-out combinatorial analyses using the DDPP on 5 patients to determine the PFS of the 6th patient with a significant accuracy despite the small number of patients.
All the rest is in the paper.
Several conclusions from our journey
- WINTHER is a gold mine for transcriptomic investigations due to the availability of patient tumor and analogous normal tissue genomics, transcriptomics and clinical outcomes for each patient. It was the first study pioneered by the WIN Consortium that assembles 35 world-class academic medical centers, industries (pharmaceutical and diagnostic companies), research organizations and patient advocates spanning 19 countries and 4 continents, aligned to launch trials using its genomics and transcriptomics biomarker platform to bolster Precision Oncology across the world. The WINTHER study was one of the last projects of our late Chairman Emeritus Dr. John Mendelsohn (previously also the President of MD Anderson Cancer Center) and Vice Chairman Prof. Thomas Tursz (previously also General Director of Gustave Roussy) who founded the organization.
- Fresh frozen biopsies are so far the only way to perform reliable transcriptomics.
- DDPP is potentially a new global biomarker model that can apply to any type of drug alone or in combination, agnostic of tumor type, and can lead, pending further prospective validation, to a new approach to optimal treatment selection for patients with cancer.
Most importantly, DDPP and the predictive ability of transcriptomics will need to be validated prospectively in larger datasets.
Written by Vladimir Lazar
Chief Scientific and Operating Officer, WIN Consortium
Genomeweb: Transcriptomic Algorithm Shows Ability to Predict Progression-Free Survival in Cancer Patients
NEW YORK – Researchers within the Worldwide Innovative Networking (WIN) Consortium are pushing ahead with research to try to show that a transcriptomic algorithm can more precisely predict the extent to which cancer patients might benefit from targeted treatments or immunotherapy compared to genomic biomarkers.
Apr 30, 2021 Turna Ray
NEW YORK (GenomeWeb)
NEW YORK – Researchers within the Worldwide Innovative Networking (WIN) Consortium are pushing ahead with research to try to show that a transcriptomic algorithm can more precisely predict the extent to which cancer patients might benefit from targeted treatments or immunotherapy compared to genomic biomarkers.
In a paper published in NPJ Precision Oncology this week, researchers co-led by Vladimir Lazar, WIN's chief scientific and operating officer, described the development of a prototype “digital display precision predictor,” or DDPP, which after further prospective validation, could provide insights into not just whether patients might respond to certain treatments, which is what current companion tests assessing genomic biomarkers assess, but also estimate the extent of their outcomes on these drugs.
WIN is an effort that brings together physician-scientists from cancer centers, drugmakers, and technology companies around the world to advance precision cancer research. A few years ago, WIN researchers conducted one of the first trials, called WINTHER, that used transcriptomics to prospectively match patients to drugs. That study didn't reach its primary endpoint, but it showed that transcriptomics can match patients to drugs when DNA sequencing cannot. It also was instrumental in the development of the DDPP, which Lazar presented as a novel biomarker concept in precision oncology.
Currently, oncologists hoping to identify personalized treatment options for their patients typically test their tumors for specific genomic aberrations using a variety of methods including immunohistochemistry, fluorescence in situ hybridization, PCR, and next-generations sequencing. And while testing for overexpression of certain genes, such as HER2, is standard of care in some tumor types, such as breast and stomach cancers, currently, oncologists are not fully sequencing the RNA in tumor and normal cells to gain a comprehensive picture of how gene expression may be harnessed to direct cancer treatment.
When patients come to oncologists for the first time suspecting cancer, Lazar said, the top questions on their minds are: Do they have cancer? Is there a treatment? And will they die of their disease? In his view, current biomarker testing methods oncologists are using to identify treatment options for patients aren't very precise.
Although DNA biomarker testing can tell you whether or not a patient is likely to respond to treatment, using these tests it is impossible to gauge the extent of a patient's response. Two patients with the same EGFR mutation driving their lung cancers may both initially respond to an EGFR inhibitor, but one patient may experience recurrence much sooner than the other.
“The major difference between DDPP and currently used technologies is that [DDPP] doesn't say 'yes' or 'no' in terms of treatment response but predicts the duration of the progression-free survival,” Lazar explained. For example,
“We're not saying whether patients will or won't response to everolimus [Novartis' Afinitor], but we can predict whether or not they will progress on therapy after six months, 12 months, 24 months or more. This is totally different.”
In fact, in the previously reported WINTHER trial, one patient treated with the mTOR inhibitor everolimus lived without cancer progression for more than five years, but had no genomic tumor alterations, for example, in the PI2K/AKT/mTOR pathway that might explain the exceptional response. In comparison, patients with much shorter progression-free survival with gastrointestinal tract neuroendocrine tumors did have mutations in this pathway but didn't respond all that well, likely due to co-occurring resistance alterations.
To address the shortcomings of DNA biomarkers to fully explain the differences in responses seen in WINTHER, Lazar and colleagues described in the NPJ Precision Oncology paper DDPP, a transcriptomics-based algorithm that can be attuned to any drug under consideration for a patient. They demonstrated this by creating a predictive algorithm for patients likely to benefit from everolimus using data from the WINTHER trial. To develop the algorithm, researchers first sifted through the literature and identified a list of genes associated with treatment response.
In the case of everolimus, the researchers identified 17 genes in the MTOR pathway that bind to everolimus. They then characterized the differential expression of each of these genes in the tumor and normal samples of six patients in the WINTHER trial who received everolimus and correlated this differential expression with their progression-free survival on the drug.
Lazar and colleagues ranked the individual genes in order of significance in terms of their association with progression-free survival and built a predictor by adding the genes one by one to the algorithm and assessing its ability to predict progression-free survival until adding genes no longer improved the DDPP's predictive abilities. The iteration of the predictor that performed optimally in being able to predict progression-free survival in the six WINTHER patients included eight genes: AKT2, TSC1, FKB-12, TSC2, RPTOR, RHEB, PIK3CA, and PIK3CB.
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