In metastatic colorectal cancer, mutation testing for KRAS exon 2 is widely implemented to select patients with wild-type tumors for treatment with the monocloncal anti-EGFR antibodies cetuximab and panitumumab. The added predictive value of additional biomarkers in the RAS-RAF-MAPK and PI3K-AKT-mTOR pathways in colorectal cancer is uncertain, which led us to systematically review the impact of alterations in KRAS (outside of exon 2), NRAS, BRAF, PIK3CA and PTEN in relation to the clinical benefit from anti-EGFR treatment. In total, 22 studies that include 2395 patients formed the basis for a meta-analysis on alterations in KRAS exons 3 and 4, NRAS, BRAF, and PIK3CA and PTEN and outcome of anti-EGFR treatment. Odds ratios for objective response rate (ORR) and hazard ratios (HR) for progression-free survival (PFS) and overall survival (OS) were calculated. Mutations in KRAS exons 3 and 4, BRAF, PIK3CA and non-functional PTEN (mutations or loss of protein expression) significantly predicted poor ORR (OR = 0.26, OR = 0.29, OR = 0.39, and OR = 0.41, respectively). Significantly shorter PFS applied to mutations in KRAS exons 3 and 4 (HR = 2.19), NRAS (HR = 2.30) and BRAF (HR = 2.95) and non-functional PTEN (HR = 1.88). Significantly shorter OS applied to mutations in KRAS exons 3 and 4 (HR = 1.78), NRAS (HR = 1.85), BRAF (HR = 2.52), PIK3CA (HR = 1.43) and alterations in PTEN (HR = 2.09). Meta-analysis suggests that mutations in KRAS exons 3 and 4, NRAS, BRAF and PIK3CA and non-functional PTEN predict resistance to anti-EGFR therapies and demonstrates that biomarker analysis beyond KRAS exon 2 should be implemented for prediction of clinical benefit from anti-EGFR antibodies in metastatic colorectal cancer.

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole-genome sequencing (WGS). Here we now introduce MRD-EDGE, a machine-learning-guided WGS ctDNA single-nucleotide variant (SNV) and copy-number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGE^SNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by ~300× compared to previous WGS error suppression. MRD-EDGE^CNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, vastly expanding its applicability within solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in TF in response to neoadjuvant immunotherapy in lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGE^SNV enables plasma-only (non-tumor-informed) disease monitoring in advanced melanoma and lung cancer, yielding clinically informative TF monitoring for patients on immune-checkpoint inhibition.

Ovarian cancer is a leading cause of death for women worldwide, in part due to ineffective screening methods. In this study, we used whole-genome cell-free DNA (cfDNA) fragmentome and protein biomarker [cancer antigen 125 (CA-125) and human epididymis protein 4 (HE4)] analyses to evaluate 591 women with ovarian cancer, with benign adnexal masses, or without ovarian lesions. Using a machine learning model with the combined features, we detected ovarian cancer with specificity >99% and sensitivities of 72%, 69%, 87%, and 100% for stages I to IV, respectively. At the same specificity, CA-125 alone detected 34%, 62%, 63%, and 100%, and HE4 alone detected 28%, 27%, 67%, and 100% of ovarian cancers for stages I to IV, respectively. Our approach differentiated benign masses from ovarian cancers with high accuracy (AUC = 0.88, 95% confidence interval, 0.83–0.92). These results were validated in an independent population. These findings show that integrated cfDNA fragmentome and protein analyses detect ovarian cancers with high performance, enabling a new accessible approach for noninvasive ovarian cancer screening and diagnostic evaluation. Significance: There is an unmet need for effective ovarian cancer screening and diagnostic approaches that enable earlier-stage cancer detection and increased overall survival. We have developed a high-performing accessible approach that evaluates cfDNA fragmentomes and protein biomarkers to detect ovarian cancer.