Manny D Bacolod   Assistant Professor of Microbiology and Immunology

A. Cancer genomics, epigenomics, and public datasets

Without a doubt, our current understanding of cancer biology and all its clinical implications  is heavily influenced  by the advent of modern genomics. Various genome-wide analytical tools  (both sequencing- and array-based technologies) interrogating  molecular attributes such as expression (mRNA, miRNA, lncRNA), copy number, CpG methylation, sequence variations (mutation, SNPs), and  DNA-protein interactions, have led to discovery of:  a)  genes crucial to cancer progression; and b) molecular biomarkers of cancer progressionearly detectionprognosispredisposition, and therapeutic response, as well as new targets for drugs. In addition to ushering  in new discoveries, genomics data have also validated (and at times corrected)  much of  our prior knowledge regarding cancer (such as   functionalitiespathwaysregulatory mechanisms, and diagnostic values  associated with  a given gene). Fortunately,  there are now tens of thousands of  cancer-related genomic datasets  which are  publicly available (e.g. TCGA, GEO, ENCODE).  TCGA datasets are particularly interesting since the molecular profiling data are integrated, and are accompanied by very comprehensive clinico-pathological information. I strongly believe that many of the important, clinically-relevant, but yet-to-be- discovered knowledge regarding cancer are just buried in the depths of publicly available cancer genomic datasets.

 

B. Current projects and interests

Having been previously involved in a wide range of cancer-related research (chemical carcinogenesis and DNA repair, therapeutics and drug resistance, diagnostics, molecular genetics), I am currently interested in employing  predictive approaches that can be applied to various topics in cancer. The eventual aim is  for these bioinformatic observations to be experimentally validated, clinically translated (for some), and IP protected (for some).  Below are some of the ongoing (and recent) projects.

1.  Cancer biomarker prediction.

● Bionformatic identification of CpG methylation  markers for blood-based early cancer detection.  We then develop highly sensitive assays, based on technologies   invented  in Francis Barany’s group (Weill Cornell). Although markers have been identified for almost every cancer type, the assays under development are intended for early detection of colorectal cancer, breast cancer, and ovarian cancer.  The projects received support from Acuamark Diagnostics and Earlier.org.

● Bioinformatic identification of other potential (and non-CpG), blood-based, early cancer molecular markers such as miRNA, lncRNA, and secreted proteins.

● Rationalization of the prognostic values of copy number-driven gene dysregulations in cancer.

● Re-analysis of public genomic datasets to identify potential radio-immunoassay-targeted surface proteins in metastasized cancer cells.

● Conceptualization of  how genomic autozygosity and somatic uniparental disomy may contribute to cancer predisposition and progression.

 

2. Immune infiltration in cancer: predictive models using public genomic datasets. Bioinformatic analyses of public genomic datasets may also help elucidate the biology of immune infiltration in cancer, as well as  identify biomarkers associated with immunotherapeutic response.  The following are some of the  ongoing projects:

● Defining the immune-related pathways that are dysregulated  in various solid tumors

● Identification of epigenetic markers which can potentially predict patient response to checkpoint inhibitors

● Bioinformatic modeling of  epigenetically-driven transcription of genes crucial to T cell activation and proliferation (e.g. CTLA4, PD1, CD3 genes, PRF1.)

 

3. Biology of cancer progression and metastasis (predictive approaches). Various questions related to cancer biology are being addressed through integrated analyses of public genomic datasets.  Among the  bioinformatic/statistical approaches employed are virtual gene over-expression/repression, gene set enrichment analysis, and transcription/methylation correlation analysis. These bioinformatic results are then experimentally verified by a number of collaborators from VCU (group of Paul Fisher), MSKCC, and WCM. Current and recent topics of interest,   include the following:

● Molecular pathways and functionalities associated with the metastasis-promoting gene MDA9 (with particular focus on glioma and neuroblastoma)

● Modeling the epigenetic regulation of  MDA9

● Predicting the mRNA transcripts (including oncogenes)  regulated by  hPNPase

● Molecular pathways associated with IDH1 mutation in glioma

● The interaction between the oncogenes AEG1 and AKT2, and its prognostic relevance in glioma

 

 4. Cancer therapeutics and drug resistance. Other ongoing projects concern cancer therapeutics and drug resistance.  Bioinformatic analyses (using public genomic datasets, as well as drug databases) are  conducted for:

●  Prediction of  potential proteins that can be targeted  using the  protein  degradation approach

●  Modeling the epigenetic regulation  of MGMT (an important  factor of resistance against alkylating/methylating chemotherapy drugs)

  • cancer epigenomics
  • cancer genomics

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  • cancer genomics, cancer epigenomics, early cancer detection, public genomic datasets, bioinformatics, cancer drug resistance, metastasis

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Primary Email

  • mdb2005@med.cornell.edu