publication date: Apr. 3, 2020

Clinical Roundup

Keytruda significantly improves PFS as first-line treatment in colorectal cancer indication

The phase III KEYNOTE-177 trial evaluating first-line treatment of Keytruda in patients with microsatellite instability-high or mismatch repair deficient unresectable or metastatic colorectal cancer met one of its dual primary endpoints of progression-free survival.

Merck sponsors Keytruda.

Based on an interim analysis conducted by an independent data monitoring committee, Keytruda monotherapy demonstrated a statistically significant and clinically meaningful improvement in PFS compared with chemotherapy (investigator’s choice of mFOLFOX6 or FOLFIRI, with or without bevacizumab or cetuximab). Based on the committee’s recommendation, the study will continue without changes to evaluate overall survival, the other dual primary endpoint. The safety profile of Keytruda in this trial was consistent with previously reported studies, and no new safety signals were identified.

“These head-to-head data with Keytruda are the first time a single-agent, anti-cancer therapy, and particularly an anti-PD-1 monotherapy, achieved a statistically significant improvement in progression-free survival over chemotherapy, including the current standard of care regimen of mFOLFOX6 plus bevacizumab, in patients with MSI-H colorectal cancer,” Roy Baynes, senior vice president and head of global clinical development, and chief medical officer of Merck Research Laboratories, said in a statement.

In May 2017, Keytruda became the first cancer therapy approved by FDA for use based on a biomarker, regardless of tumor type, in previously treated patients with MSI-H or dMMR solid tumors.

 

Phase III cancer vaccine trial meets primary endpoint

The primary endpoint was met in a predefined step-1 analysis of a phase III trial in non-small cell lung cancer patients treated with Tedopi, a neoepitope cancer vaccine.

Data showed a 12 month-survival rate of 46% for patients treated with Tedopi versus 36% for patients in the chemotherapy control arm. Enrollment in the Step-2 portion of the phase III trial have been stopped due to concerns over COVID-19.

Instead, there will be further analysis of step-1 data in parallel with discussions with regulatory agencies on a pathway to approval.

Tedopi is being developed in a phase 3 trial in NSCLC for patients who have failed to respond on PD1/PDL1 checkpoint inhibitor treatments, the current standard of care. About 30% of NSCLC patients don’t respond to checkpoint inhibitors and this patient population lacks approved treatment options. Tedopi is also currently being explored in a phase II trial in pancreatic cancer.

 

Caris Life Sciences develops AI-powered predictor of chemotherapy sensitivity in MCC

Caris Life Sciences launched MI FOLFOXai, an artificial intelligence-based predictor of response to FOLFOX chemotherapy in metastatic colorectal cancer that demonstrated approximately 50% improvement in overall survival across two independent validation studies.

The AI-powered predictor is using Caris Molecular Intelligence tumor profiling results, and is intended to gauge a patient’s likelihood of benefit from FOLFOX as a first-line regimen in combination with bevacizumab.

MI FOLFOXai was validated using two independent data sets to compare the increased benefit arm to the decreased benefit arm. The first study was a blinded, prospective analysis from retrospectively tested samples from the randomized phase III TRIBE2 study. This study showed a median overall survival improvement of 6.9 months. The second study involved several hundred cases with real-world evidence that showed a median overall survival increase of 11.8 months.

MI FOLFOXai was developed using a subset of results from the company’s proprietary Caris Molecular Intelligence platform, which includes next generation sequencing for DNA mutations, copy number alterations, insertions/deletions; whole transcriptome sequencing for RNA fusions and variant transcripts; and protein testing via immunochemistry. Machine learning algorithms were then used to create a molecular signature that was validated using the two independent data sets to compare the increased benefit arm to the decreased benefit arm.

Copyright (c) 2020 The Cancer Letter Inc.