Precision combination therapies based on recurrent oncogenic co-alterations

AbstractCancer cells depend on multiple driver alterations whose oncogenic effects can be suppressed by drug combinations. Discovery of effective combination therapies is challenging due to the complexity of the biomolecular landscape of drug responses. Here, we developed the method REFLECT (REcurrent Features Leveraged for Combination Therapies), which integrates machine learning and cancer informatics algorithms. The method maps recurrent co-alteration signatures from multi-omic data across patient cohorts to combination therapies. Using the REFLECT framework, we generated a precision therapy resource matching 2,201 drug combinations to co-alteration signatures across 201 cohorts stratified from 10,392 patients and 33 cancer types. We validated that REFLECT-predicted combinations introduce significantly higher therapeutic benefit through analysis of independent data from comprehensive drug screens. In patient cohorts with immunotherapy response markers, HER2 activation and DNA repair aberrations, we identified therapeutically actionable co-alteration signatures shared across patient sub-cohorts. REFLECT provides a framework to design combination therapies tailored to patient cohorts in data-driven clinical trials.

Download Full-text

PI3K Inhibitors in Cancer: Clinical Implications and Adverse Effects

International Journal of Molecular Sciences ◽

10.3390/ijms22073464 ◽

2021 ◽

Vol 22 (7) ◽

pp. 3464

Author(s):

Rosalin Mishra ◽

Hima Patel ◽

Samar Alanazi ◽

Mary Kate Kilroy ◽

Joan T. Garrett

Keyword(s):

Clinical Trials ◽

Intracellular Signaling ◽

Pi3k Pathway ◽

Combination Therapies ◽

Intracellular Signaling Pathway ◽

Pi3k Inhibitors ◽

Promising Therapeutic Target ◽

Cancer Cell Survival ◽

Current Therapies ◽

Cancer Types

The phospatidylinositol-3 kinase (PI3K) pathway is a crucial intracellular signaling pathway which is mutated or amplified in a wide variety of cancers including breast, gastric, ovarian, colorectal, prostate, glioblastoma and endometrial cancers. PI3K signaling plays an important role in cancer cell survival, angiogenesis and metastasis, making it a promising therapeutic target. There are several ongoing and completed clinical trials involving PI3K inhibitors (pan, isoform-specific and dual PI3K/mTOR) with the goal to find efficient PI3K inhibitors that could overcome resistance to current therapies. This review focuses on the current landscape of various PI3K inhibitors either as monotherapy or in combination therapies and the treatment outcomes involved in various phases of clinical trials in different cancer types. There is a discussion of the drug-related toxicities, challenges associated with these PI3K inhibitors and the adverse events leading to treatment failure. In addition, novel PI3K drugs that have potential to be translated in the clinic are highlighted.

Download Full-text

An overview of the effective combination therapies for the treatment of breast cancer

Biomaterials ◽

10.1016/j.biomaterials.2016.04.027 ◽

2016 ◽

Vol 97 ◽

pp. 34-50 ◽

Cited By ~ 59

Author(s):

Cristina Núñez ◽

José Luis Capelo ◽

Gilberto Igrejas ◽

Amparo Alfonso ◽

Luis M. Botana ◽

...

Keyword(s):

Breast Cancer ◽

Combination Therapies ◽

Effective Combination

Download Full-text

Multi-omic data helps improve prediction of personalised tumor suppressors and oncogenes

10.1101/2022.01.13.476163 ◽

2022 ◽

Author(s):

Malvika Sudhakar ◽

Raghunathan Rengaswamy ◽

Karthik Raman

Keyword(s):

Tumour Progression ◽

Suppressor Gene ◽

Tumour Suppressor Gene ◽

Driver Mutations ◽

Cancer Type ◽

Expression Data ◽

Multiple Cancer ◽

Driver Genes ◽

Cancer Types ◽

Omic Data

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require a large number of samples to produce reliable results. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumour progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify the perturbation in the network. Our method is the first machine learning model to classify genes as tumour suppressor gene (TSG), oncogene (OG) or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalised Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalised driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labelling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared to mutation and expression data, achieving an accuracy >0.99 for BRCA, LUAD and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD and LUAD cancer types reveal commonly altered genes such as TP53, and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9 and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalised driver genes as TSGs and OGs and also identifies rare driver genes. PIVOT is available at https://github.com/RamanLab/PIVOT.

Download Full-text

Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival

10.1101/267245 ◽

2018 ◽

Cited By ~ 2

Author(s):

Daniele Ramazzotti ◽

Avantika Lal ◽

Bo Wang ◽

Serafim Batzoglou ◽

Arend Sidow

Keyword(s):

Molecular Mechanisms ◽

Molecular Subtypes ◽

Point Mutations ◽

Integrated Analysis ◽

Tumor Type ◽

Cancer Subtypes ◽

Cancer Types ◽

Copy Number Changes ◽

Omic Data

Outcomes for cancer patients vary greatly even within the same tumor type, and characterization of molecular subtypes of cancer holds important promise for improving prognosis and personalized treatment. This promise has motivated recent efforts to produce large amounts of multidimensional genomic (‘multi-omic’) data, but current algorithms still face challenges in the integrated analysis of such data. Here we present Cancer Integration via Multikernel Learning (CIMLR), a new cancer subtyping method that integrates multi-omic data to reveal molecular subtypes of cancer. We apply CIMLR to multi-omic data from 36 cancer types and show significant improvements in both computational efficiency and ability to extract biologically meaningful cancer subtypes. The discovered subtypes exhibit significant differences in patient survival for 27 of 36 cancer types. Our analysis reveals integrated patterns of gene expression, methylation, point mutations and copy number changes in multiple cancers and highlights patterns specifically associated with poor patient outcomes.

Download Full-text

Big Data Driven Oriented Graph Theory Aided tagSNPs Selection for Genetic Precision Therapy

IEEE Access ◽

10.1109/access.2018.2886926 ◽

2019 ◽

Vol 7 ◽

pp. 3746-3754

Author(s):

Tianshuo Cong ◽

Jingjing Wang ◽

Sanghai Guan ◽

Yifei Mu ◽

Tong Bai ◽

...

Keyword(s):

Big Data ◽

Graph Theory ◽

Oriented Graph ◽

Data Driven ◽

Precision Therapy ◽

Selection For

Download Full-text

Strategic Combination Therapies for Ovarian Cancer

Current Cancer Drug Targets ◽

10.2174/1568009620666200511084007 ◽

2020 ◽

Vol 20 (8) ◽

pp. 573-585

Author(s):

Xinran Li ◽

Angel S.N. Ng ◽

Victor C.Y. Mak ◽

Karen K.L. Chan ◽

Annie N.Y. Cheung ◽

...

Keyword(s):

Ovarian Cancer ◽

Targeted Therapies ◽

Cancer Biology ◽

Gynecologic Cancer ◽

Antiangiogenic Agents ◽

Combination Therapies ◽

Multiple Cancer ◽

Therapeutic Modalities ◽

Cancer Pathways ◽

Effective Combination

Ovarian cancer remains the leading cause of gynecologic cancer-related deaths among women worldwide. The dismal survival rate is partially due to recurrence after standardized debulking surgery and first-line chemotherapy. In recent years, targeted therapies, including antiangiogenic agents or poly (ADP-ribose) polymerase inhibitors, represent breakthroughs in the treatment of ovarian cancer. As more therapeutic agents become available supplemented by a deeper understanding of ovarian cancer biology, a range of combination treatment approaches are being actively investigated to further improve the clinical outcomes of the disease. These combinations, which involve DNA-damaging agents, targeted therapies of signaling pathways and immunotherapies, simultaneously target multiple cancer pathways or hallmarks to induce additive or synergistic antitumor activities. Here we review the preclinical data and ongoing clinical trials for developing effective combination therapies in treating ovarian cancer. These emerging therapeutic modalities may reshape the treatment landscape of the disease.

Download Full-text

A Data Driven Approach to Profile Potential SARS-CoV-2 Drug Interactions Using TylerADE (Preprint)

10.2196/preprints.19979 ◽

2020 ◽

Author(s):

Robert P Schumaker ◽

Michael A Veronin ◽

Trevor Rohm ◽

Matthew C Boyett ◽

Rohit R Dixit

Keyword(s):

Adverse Drug Reaction ◽

Drug Reaction ◽

Adverse Drug Event ◽

Treatment Guidelines ◽

Drug Efficacy ◽

Drug Combinations ◽

Data Driven ◽

Drug Event ◽

Data Driven Approach ◽

Reaction Severity

UNSTRUCTURED We use a data driven approach on a cleaned FAERS database to determine the adverse drug reaction severity of several covid-19 drug combinations and further investigate their safety for vulnerable populations such as individuals 65 years and older. Our key findings include 1. hydroxychloroquine/chloroquine is associated with increased adverse drug event severity versus other drug combinations already not recommended by NIH treatment guidelines, 2. hydroxychloroquine/azithromycin is associated with lower adverse drug event severity among older populations and 3. lopinavir/ritonavir has lower adverse reaction severity among toddlers. While this approach does not consider drug efficacy, it can help prioritize clinical trials for drug combinations by focusing on those combinations with decreased adverse drug reaction severity.

Download Full-text