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Background Blood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer. Methods Whole-genome sequencing was performed on cfDNA extracted from plasma samples ( N  = 546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validations to assess generalization performance. Results In a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91–0.93) with a mean sensitivity of 85% (95% CI 83–86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance. Conclusions A machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

Primary resistance of HIV to zidovudine was first described in 1992 and has since been identified in up to 10% of antiretroviral-naive patients in some centers. Primary resistance to nevirapine has also been reported, but the prevalence of primary resistance to other agents in widespread use in clinical practice in unknown. We now report two cases of primary lamivudine resistance in two antiretroviral-naive patients who presented with acute HIV infection.

A prospective blinded study was conducted to determine whether immunological differences exist between patients receiving potent antiretroviral therapy who are able to achieve and maintain an undetectable virus load (<50 copies/mL) and those who are not. Eleven patients receiving protease inhibitor-containing antiretroviral therapy were studied for 1 year. After analysis of all baseline samples, patient virus load was disclosed, and patients were classified as suppressors (those who maintained undetectable virus load for 1 year) and nonsuppressors. Baseline virus load and CD4+ T cell count did not differ significantly between the 2 groups. Levels of RANTES production by CD4+ and CD8+ T cells and CD8-mediated inhibition of human immunodeficiency virus type 1 gene expression before initiation of antiretroviral therapy were significantly associated with an undetectable virus load maintained for 1 year (P < .05). Thus, a functionally intact T cell-mediated immune system at the time of initiation of potent antiretroviral therapy may predict long-term virus suppression.

A prospective blinded study was conducted to determine whether immunological differences exist between patients receiving potent antiretroviral therapy who are able to achieve and maintain an undetectable virus load (<50 copies/mL) and those who are not. Eleven patients receiving protease inhibitor-containing antiretroviral therapy were studied for 1 year. After analysis of all baseline samples, patient virus load was disclosed, and patients were classified as suppressors (those who maintained undetectable virus load for 1 year) and nonsuppressors. Baseline virus load and CD4+ T cell count did not differ significantly between the 2 groups. Levels of RANTES production by CD4+ and CD8+ T cells and CD8-mediated inhibition of human immunodeficiency virus type 1 gene expression before initiation of antiretroviral therapy were significantly associated with an undetectable virus load maintained for 1 year (P<.05). Thus, a functionally intact T cell-mediated immune system at the time of initiation of potent antiretroviral therapy may predict long-term virus suppression.

The short-term effects of stavudine (d4T) plus lamivudine (3TC) were evaluated among 48 human immunodeficiency virus-infected patients for whom zidovudine therapy had failed or who could not tolerate zidovudine. Patients were followed for 8 weeks after initiation of open-label d4T plus 3TC. Four patients discontinued therapy, because of neutropenia (1), hepatitis (1), or neuropathy (2). Reduction in virus load was -0.86 (+0.3 to -3.4) log10 copies/mL and CD4 cell increase was 30 (-100 to +290) cells/mm3. Virologic response was associated with a higher CD4 cell count, no prior exposure to d4T and 3TC, and no previous AIDS-defining illness. Virus load reduction for patients naive to 3TC and d4T was -1.47 (-0.14 to -3.37) log10 copies/mL. Short-term use of d4T plus 3TC is safe, well-tolerated, and associated with virologic and substantial immunologic benefits. Further evaluation of d4T and 3TC in combination is warranted.

Blood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer. Whole-genome sequencing was performed on cfDNA extracted from plasma samples (N=546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validation to assess generalization performance. In a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance. A machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.