Camilo Ruiz is a fifth-year bioengineering PhD student focused on machine learning for drug discovery. Prior to his PhD, Camilo completed his MS in Computer Science at Stanford and a MPhil in Biological Sciences at Cambridge University as a Gates-Cambridge Scholar. Camilo also has a BS from MIT in Electrical Engineering & Computer Science and in Bioengineering. Additionally, Camilo worked in McKinsey & Company’s Pharmaceutical and Medical Products Practice. Camilo has won numerous awards for his research including the Siebel Fellowship, NSF GRFP Fellowship, the Stanford EDGE Fellowship, and the Gates-Cambridge Scholarship.
Identification of disease treatment mechanisms through the multiscale interactome.
2021; 12 (1): 1796
Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins. How drugs restore these functions, however, is often unknown as a drug's therapeutic effects are not limited to the proteins that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach to explain disease treatment. We integrate disease-perturbed proteins, drug targets, and biological functions into a multiscale interactome network. We then develop a random walk-based method that captures how drug effects propagate through a hierarchy of biological functions and physical protein-protein interactions. On three key pharmacological tasks, the multiscale interactome predicts drug-disease treatment, identifies proteins and biological functions related to treatment, and predicts genes that alter a treatment's efficacy and adverse reactions. Our results indicate that physical interactions between proteins alone cannot explain treatment since many drugs treat diseases by affecting the biological functions disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide a general framework for explaining treatment, even when drugs seem unrelated to the diseases they are recommended for.
View details for DOI 10.1038/s41467-021-21770-8
View details for PubMedID 33741907
Targeted sequencing in DLBCL, molecular subtypes, and outcomes: a Haematological Malignancy Research Network report.
Based on the profile of genetic alterations occurring in tumor samples from selected diffuse-large-B-cell-lymphoma (DLBCL) patients, two recent whole exome sequencing studies proposed partially overlapping classification systems. Using clustering techniques applied to targeted sequencing data derived from a large unselected population-based patient cohort with full clinical follow-up (n=928), we investigated whether molecular subtypes can be robustly identified using methods potentially applicable in routine clinical practice. DNA extracted from DLBCL tumors diagnosed in patients residing in a catchment population of ~4 million (14 centers), were sequenced with a targeted 293-gene hematological-malignancy panel. Bernoulli mixture-model clustering was applied, and the resulting subtypes analyzed in relation to their clinical characteristics and outcomes. Five molecular subtypes were resolved, termed MYD88, BCL2, SOCS1/SGK1, TET2/SGK1 and NOTCH2, along with an unclassified group. The subtypes characterized by genetic alterations of BCL2, NOTCH2 and MYD88 respectively recapitulated recent studies showing good, intermediate and poor prognosis respectively. The SOCS1/SGK1 subtype showed biological overlap with primary mediastinal B-cell lymphoma and conferred excellent prognosis. Although not identified as a distinct cluster, NOTCH1 mutation was associated with poor prognosis. The impact of TP53 mutation varied with genomic subtypes, conferring no effect in the NOTCH2 subtype and poor prognosis in the MYD88 subtype. Our findings confirm the existence of molecular subtypes of DLBCL, providing evidence that genomic tests have prognostic significance in non-selected DLBCL patients. The identification of both good and poor risk subtypes in R-CHOP treated patients clearly demonstrate the clinical value of the approach; confirming the need for a consensus classification.
View details for DOI 10.1182/blood.2019003535
View details for PubMedID 32187361
- A longitudinal big data approach for precision health NATURE MEDICINE 2019; 25 (5): 792-+
A longitudinal big data approach for precision health.
2019; 25 (5): 792–804
Precision health relies on the ability to assess disease risk at an individual level, detect early preclinical conditions and initiate preventive strategies. Recent technological advances in omics and wearable monitoring enable deep molecular and physiological profiling and may provide important tools for precision health. We explored the ability of deep longitudinal profiling to make health-related discoveries, identify clinically relevant molecular pathways and affect behavior in a prospective longitudinal cohort (n = 109) enriched for risk of type 2 diabetes mellitus. The cohort underwent integrative personalized omics profiling from samples collected quarterly for up to 8 years (median, 2.8 years) using clinical measures and emerging technologies including genome, immunome, transcriptome, proteome, metabolome, microbiome and wearable monitoring. We discovered more than 67 clinically actionable health discoveries and identified multiple molecular pathways associated with metabolic, cardiovascular and oncologic pathophysiology. We developed prediction models for insulin resistance by using omics measurements, illustrating their potential to replace burdensome tests. Finally, study participation led the majority of participants to implement diet and exercise changes. Altogether, we conclude that deep longitudinal profiling can lead to actionable health discoveries and provide relevant information for precision health.
View details for PubMedID 31068711