Table 3.

In silico size-based weighting of potential somatic mutations for ctDNA and tumour detection

Publication	Methods	Cancer (sample size)	Results
Mouliere et al., 2018 [68]	sWGS RF algorithm that included proportion of fragments in defined size ranges and tMAD score	“High ctDNA” cancers from melanoma, ovarian, lung, colorectal, cholangiocarcinoma, and other (N = 68) “Low ctDNA” cancers from renal, brain, bladder, and pancreas (N = 57) Healthy controls (N = 26)	In high ctDNA cancers, an RF model yielded an AUC of 0.994 for distinguishing cancer patients from controls In low ctDNA cancers, an RF model yielded an AUC of 0.914 for distinguishing cancer patients from controls An RF model using fragmentation features alone (leaving out tMAD score) yielded AUCs of 0.989 and 0.891 for cancer types with a high and low amount of ctDNA, respectively
Wan et al., 2020 [121]	Tumour-informed, patient-specific, custom-capture panels INVAR weights mutant reads across all patient-specific mutation loci based on the empirical distribution of mutant fragments in all other samples in the cohort being studied to give a size range enriched in cancer greater weight	Stage II–III melanoma after complete resection (N = 38) Stage IV melanoma (N = 9; 52 samples)	ctDNA detected in 11 stage II–III patients (28.9%, specificity at 98.6%), and the integrated MAF in 9 of the 11 patients was below the 95% LOD for a “perfect” single-locus assay based on ccfDNA input amount (AUC = 0.64) ctDNA detected in the baseline samples of 9 stage IV patients (100%) ctDNA detected in 50 of 52 treatment and follow-up samples from 9 stage IV patients where the integrated MAF in 15 of the 50 samples was below the 95% LOD for a “perfect” single-locus assay based on ccfDNA input amount
		Stage I–IV breast cancer (N = 7) NSCLC (N = 19) Renal tumours (N = 24) Brain tumours (N = 8)	16 samples from stage I–II breast cancer, sensitivity of 62.5% at specificity of 90% (AUC = 0.81) 19 samples from stage IV breast cancer, sensitivity of 100% at specificity of 100% (AUC = 1.00) 8 patients with grade II–IV brain tumours, sensitivity of 75% at specificity of 90% (AUC = 0.92) 24 patients with stage I–IV renal tumours, sensitivity of 41.7% at specificity of 90% (AUC = 0.66) 19 patients with stage I–III NSCLC, sensitivity of 63.1% at specificity of 98% (AUC = 0.80)
Smith et al., 2020 [115]	Custom-panel with patient-specific mutations detected in tumour DNA by WES and 109 genes commonly mutated in RCC INVAR	Renal tumours (benign to metastatic; N = 22)	ctDNA was detected in 12 patients (54.5%)
	Custom-panel targeting 10 genes in renal cancers INVAR	RCC (N = 43, 41 patients with metastatic disease, 35 patients with primary tumour removed)	ctDNA was detected in 8 patients (18.6%) Mean MAF was 8.3% (range 3.5–18%)
Chabon et al., 2020 [112]	Tumour-informed search using a 355-kb panel of 255 genes recurrently mutated in NSCLC Lung-CLiP – a multi-tiered machine-learning approach that includes fragment size to estimate the probability that a ccfDNA mutation is tumour derived	Stage I–II NSCLC (N = 41) Stage III NSCLC (N = 5) Risk-matched controls (N = 48)	For stage I–II patients: Sensitivity of ~30% at 98% specificity Sensitivity of ~56% at 80% specificity For stage III patients: Sensitivity of ~60% at 98% specificity Sensitivity of ~100% at 80% specificity

AUC area under the curve, ccfDNA circulating cell-free DNA, ctDNA circulating tumour DNA, INVAR INtegration of VAriant Reads, LOD limit of detection, Lung-CLiP lung cancer likelihood in plasma, MAF mutant allele frequency, NSCLC non-small cell lung cancer, RCC renal cell carcinoma, RF random forest, sWGS shallow whole genome sequencing, tMAD trimmed median absolute deviation from copy number neutrality, WES whole exome sequencing