Molecular Profiling-Selected Therapy for Treatment of Advanced Pancreaticobiliary Cancer: A Retrospective Multicenter Study

Decision impact studies, evidence of clinical utility for genomic assays in cancer: A scoping review

BACKGROUND

Decision impact studies have become increasingly prevalent in cancer prognostic research in recent years. These studies aim to evaluate the impact of a genomic test on decision-making and appear to be a new form of evidence of clinical utility. The objectives of this review were to identify and characterize decision impact studies in genomic medicine in cancer care and categorize the types of clinical utility outcomes reported.

METHODS

We conducted a search of four databases, Medline, Embase, Scopus and Web of Science, from inception to June 2022. Empirical studies that reported a "decision impact" assessment of a genomic assay on treatment decisions or recommendations for cancer patients were included. We followed scoping review methodology and adapted the Fryback and Thornbury Model to collect and analyze data on clinical utility. The database searches identified 1803 unique articles for title/abstract screening; 269 articles moved to full-text review.

RESULTS

87 studies met inclusion criteria. All studies were published in the last 12 years with the majority for breast cancer (72%); followed by other cancers (28%) (lung, prostate, colon). Studies reported on the impact of 19 different proprietary (18) and generic (1) assays. Across all four levels of clinical utility, outcomes were reported for 22 discrete measures, including the impact on provider/team decision-making (100%), provider confidence (31%); change in treatment received (46%); patient psychological impacts (17%); and costing or savings impacts (21%). Based on the data synthesis, we created a comprehensive table of outcomes reported for clinical utility.

CONCLUSIONS

This scoping review is a first step in understanding the evolution and uses of decision impact studies and their influence on the integration of emerging genomic technologies in cancer care. The results imply that DIS are positioned to provide evidence of clinical utility and impact clinical practice and reimbursement decision-making in cancer care. Systematic review registration: Open Science Framework osf.io/hm3jr.

Challenges Faced by Clinicians in the Personalized Treatment Planning: A Literature Review and the First Results of the Russian National Cancer Program

Advances in cancer molecular profiling have enabled the development of more effective approaches to the diagnosis and personalized treatment of tumors. However, treatment planning has become more labor intensive, requiring hours or even days of clinician effort to optimize an individual patient case in a trial-and-error manner. Lessons learned from the world cancer programs provide insights into ways to develop approaches for the treatment strategy definition which can be introduced into clinical practice. This article highlights the variety of breakthroughs in patients' cancer treatment and some challenges that this field faces now in Russia. In this report, we consider the key characteristics for planning an optimal clinical treatment regimen and which should be included in the algorithm of clinical decision support systems. We discuss the perspectives of implementing artificial intelligence-based systems in cancer treatment planning in Russia.

Cost-comparison analysis of a multiplatform tumour profiling service to guide advanced cancer treatment

Background

Tumor profiling is increasingly used in advanced cancer patients to define treatment options, especially in refractory cases where no standard treatment is available. Caris Molecular Intelligence (CMI) is a multiplatform tumor profiling service that is comprehensive of next-generation sequencing (NGS) of DNA and RNA, immunohistochemistry (IHC) and in situ hybridisation (FISH). The aim of this study is to compare costs of CMI-guided treatment with prior or planned treatment options in correlation with outcome results.

Methods

Retrospective data from five clinical trials were collected to define the treatment decision prior to the receipt of the CMI report (n = 137 patients). A systematic review of treatment data from 11 clinical studies of CMI (n = 385 patients) allowed a comparison of planned vs actual (n = 137) and prior vs actual (n = 229) treatment costs.

Results

Treatment plan was changed in 88% of CMI-profiled cases. The actual CMI guided treatment cost per cycle was £995 in 385 treated patients. Planned treatment costs were comparable to actual treatment costs (£979 vs £945; p = 0.7123) and prior treatment costs were not significantly different to profiling-guided treatments (£892 vs £850; p = 0.631).

Conclusions

Caris Molecular Intelligence guided treatment cost per cycle was in the range of prior or planned treatment cost/cycle. Due to beneficial overall survival the additional cost of performing CMI's multiplatform testing to the treatment costs seems to be cost-effective.

Biomarker-Driven Therapy in Metastatic Gastric and Esophageal Cancer: Real-Life Clinical Experience

BACKGROUND

Precision treatment of cancer uses biomarker-driven therapy to individualize and optimize patient care.

OBJECTIVE

To evaluate real-life clinical experience with biomarker-driven therapy in metastatic gastric and esophageal cancer in Israel.

PATIENTS AND METHODS

This multicenter retrospective cohort study included patients with metastatic gastric or esophageal cancer who were treated in the participating institutions and underwent biomarker-driven therapy. Treatment was considered to have a benefit if the ratio between the longest progression-free survival (PFS) post biomarker-driven therapy and the last PFS before the biomarker-driven therapy was ≥1.3. The null hypothesis was that ≤15% of patients gain such benefit.

RESULTS

The analysis included 46 patients (61% men; median age, 58 years; 57% with poorly-differentiated tumors). At least one actionable (i.e., predictive of response to a specific therapy) biomarker was identified for each patient. Immunohistochemistry was performed on all samples and identified 1-8 (median: 3) biomarkers per patient (most commonly: low TS, high TOPO1, high TOP2A). Twenty-eight patients received therapy after the biomarker analysis (1-4 lines). In the 1st line after biomarker analysis, five patients (18%) achieved a partial response and five (18%) stable disease; the median (range) PFS was 129 (12-1155) days. Twenty-four patients were evaluable for PFS ratio analysis; in seven (29.2%), the ratio was ≥1.3. In a one-sided exact binomial test vs. the null hypothesis, p = 0.019; therefore, the null hypothesis was rejected.

CONCLUSIONS

Our findings demonstrated that implementing biomarker-driven analysis is feasible and could provide clinical benefit for a considerable proportion (~30%) of patients with metastatic gastric or esophageal cancer.

Impact of molecular profiling on overall survival of patients with advanced ovarian cancer

OBJECTIVE

Patients with recurrent epithelial ovarian cancer (EOC) have limited treatment options. Studies have reported that biomarker profiling may help predict patient response to available treatments. This study sought to determine the value of biomarker profiling in recurrent EOC.

RESULTS

Patients in the Matched cohort had a median OS of 36 months compared to 27 months for patients in the Unmatched cohort (HR 0.62, 95% CI 0.41-0.96; p < 0.03). Individual biomarkers were analyzed, with TUBB3, and PGP prognostic for survival. Biomarker analysis also identified a molecular subtype (positive for at least two of the following markers: ERCC1, RRM1, TUBB3, PGP) with particularly poor overall survival.

METHODS

224 patients from a commercial registry (NCT02678754) with stage IIIC/IV EOC at diagnosis, or restaged to IIIC/IV EOC at the time of molecular profiling, were retrospectively divided into two cohorts based on whether or not the drugs they received matched their profile recommendations. The Matched cohort received no drugs predicted to be lack-of-benefit while the Unmatched cohort received at least one drug predicted to be lack-of-benefit. Profile biomarker/drug associations were based on multiple test platforms including immunohistochemistry, fluorescent in situ hybridization and DNA sequencing.

CONCLUSIONS

This report demonstrates the ability of multi-platform molecular profiling to identify EOC patients at risk of inferior survival. It also suggests a potential beneficial role of avoidance of lack-of-benefit therapies which, when administered, resulted in decreased survival relative to patients who received only therapies predicted to be of benefit.