The Crossroads of Precision Medicine and Therapeutic Decision-Making: Use of an Analytical Computational Platform to Predict Response to Cancer Treatments

Abstract

Metastatic cancer is a medical challenge that has been historically resistant to treatments. One area of leverage in cancer care is the development of molecularly-driven combination therapies, offering the possibility to overcome resistance. The selection of optimized treatments based on the complex molecular features of a patient’s tumor may be rendered easier by using a computer-assisted program. We used the PreciGENE® platform that uses multi-pathway molecular analysis to identify personalized therapeutic options. These options are ranked using a predictive score reflecting the degree to which a therapy or combination of therapies matches the patient’s biomarker profile. We searched PubMed from February 2010 to June 2017 for all patients described as exceptional responders who also had molecular data available. Altogether, 70 patients with cancer who had received 202 different treatment lines and who had responded (stable disease ≥12 months/partial or complete remission) to ≥1 regimen were curated. We demonstrate that an algorithm reflecting the degree to which patients were matched to the drugs administered correctly ranked the response to the regimens with a sensitivity of 84% and a specificity of 77%. The difference in matching score between successful and unsuccessful treatment lines was significant (median, 65% versus 0%, p-value <0.0001).

Profiling of circulating tumor DNA and tumor tissue for treatment selection in patients with advanced and refractory carcinoma: a prospective, two-stage phase II Individualized Cancer Treatment trial

“…Therefore, we sought to identify potential alternative treatment options and, to this end, we retrospectively analyzed seven patient profiles with the CureMatch decision support algorithm, a platform which assesses the entire molecular profile for matching combination therapies rather than evaluating individual targets for their actionability. 23 The results of these analyses were outside of the scope of the treatment algorithm in this study and were only evaluated as retrospective information. Only the results from plasma DNA analyses were considered.…”

“…The CureMatch approach represents a multi-pathway molecular analysis for the identification of personalized treatment options, which are ranked using a predictive ‘matching score’ that reflects the degree to which therapies align to a patient’s molecular profile. 23 This retrospective analysis demonstrates the complexity and diversity of existing matching algorithms that would ultimately lead to variable MTB decisions and highlights a potential shift in the current clinical trial paradigm. Hence, administering customized multi-drug treatments may represent an effective alternative to the one-aberration-one-drug model.…”

“…Sample size was calculated based on the optimal two-stage design of Chen and Ng. 21 Assuming a success (PFS of the targeted therapy ⩾1.2 × PFS of the last evidence-based drug therapy) rate of p 1 = 0.25 and p 0 = 0.1 at a significance level of α = 5% and a power of 1– β = 90%, 24 21 – 28 patients should be included in the first stage of the trial. If the number of successes were >2 with 21–24 or >3 with 25–28 patients, then a total of 57–64 patients would have to be included in the study.…”

Integrating Molecular Biomarker Inputs Into Development and Use of Clinical Cancer Therapeutics

“…Validation can be achieved through independent datasets, but ideally also involves animal experiments or clinical trials ( Deo, 2015 ). Clinical applications of machine learning analyses include the Oncotype Dx scoring in breast cancer ( Wang et al, 2019 ), and clinical trials of personalized combination therapies chosen based on predicted response ( Boichard et al, 2020 ). The application of machine learning can identify novel biomarkers from relationships not readily apparent within large data sets and will be increasingly important in new multi-omic approaches.…”

Comparison of three commercial decision support platforms for matching of next-generation sequencing results with therapies in patients with cancer

“…The CureMatch strategy differs greatly as it involves the ranking and prioritisation of alterations and treatments for implementation of combination therapy, targeting the entire aberrant profile rather than simply matching treatments to individual targets. Therapy recommendations are accompanied by a proprietary 'Matching Score' (Boichard et al 26 see details in Methods section), which prioritises therapies and generates the top three 3-drug and 2-drug combination therapies and top three monotherapies. Furthermore, it is possible to incorporate patient-specific history, such as prior treatment lines, comorbidities and medical history, into CureMatch analyses.…”