My Interest in Personalized Medicine and Cancer

Welcome to the newest blog post from the the Wellsmont Group.

This blog represents the interest and focus of Douglas P. Malinowski, the founder and executive director of the Wellsmont Group. We are focused on advances in personalized medicine and precision medicine that improve the clinical detection and management of infectious diseases and cancer.

In this post, I will briefly chronicle my interest in the area of personalized medicine in cancer. From my perspective, the application of molecular technologies to describe cancer helps elucidate the underlying molecular biology of this complex disease. In turn, this understanding helps shape our approach to the clinical characterization of cancer and the use of this information to create a personalized approach to cancer treatment.

Research Applications in Personalized Medicine and Cancer.

I have had the opportunity to work across a broad array of human cancers and related technologies associated with disease characterization and clinical laboratory testing. This work included the molecular biology of cancer, biomarker discovery, molecular profiling, molecular analysis of gene and protein expression, biomarker translational research, IVD diagnostics technologies, cell-based nucleic acid and protein detection, image analysis, and computational pathology.

Personalized Medicine and Cancer.

My professional career focused on the application of molecular diagnostic technologies to improve personalized medicine and cancer. Throughout my career, I have worked on a number of cancer-related projects which spanned multiple cancer types, various technology applications and intended clinical uses.

The list of cancers that formed the basis for my work in diagnostics and molecular technologies include:

• HPV molecular biology and oncogenesis
• Oncogenic HPV and cervical cancer (and pre-cancer)
• HPV associated non-small cell lung cancer (NSCLC)
• Bladder cancer
• Breast cancer and DCIS
• Cervical cancer and cervical pre-cancer (CIN2+)
• Colon cancer
• Endometrial cancer
• Lung cancer – especially NSCLC
• Melanoma
• Ovarian cancer
• Prostate cancer
• Thyroid cancer

Douglas P. Malinowski Background and Experience in Cancer-based Personalized Medicine.

My first introduction to the topic of precision medicine began with a strategic collaboration initiated between BD and Millennium Predictive Medicine. This collaboration focused on a wide-ranging biomarker discovery program in human cancer. The goal of the collaboration was to identify biomarkers for use in cancer testing. The applications included early detection, diagnosis and prognosis biomarkers that could extend the utility and accuracy of cancer-based assay.

This work focused on the identification of cancer biomarkers and the development of novel diagnostic tests. The application of these testing modalities was intended to improve cancer detection and molecular characterization of cancer.

Subsequent work at BD’s Women’s Health and Cancer business extended the biomarker use started with Millennium. I focused on biomarkers for use in cervical cancer and pre-cancerous conditions (cytology and histology applications). This work also included the role of HPV genotyping in cervical cancer screening and the use of cervical cytology and triage methods to improve risk assessment and management of cervical cancer screening results.

My work at PreciseDx focused on the application of computational pathology and AI algorithm validation for the characterization of early-stage breast cancer. The clinical application was focused on risk stratification for disease recurrence in early-stage invasive breast cancer.

On-going Interest and Study in Cancer-based Personalized Medicine.

The complexity of cancer and the underlying molecular characterization of the disease has enabled an individualized approach to patient management. This includes both accurate diagnosis, prediction of clinical outcome and improved selection of effective treatment options. This is the essence of personalized medicine.

Robust molecular characterization of cancer also creates the opportunity to apply and/or develop target specific therapies to treat the disease. The alignment between molecular characterization of disease and the clinical context of disease presentation creates opportunities to advance the utility and impact of precision medicine.

My introduction to computational pathology while at PreciseDx led to further self-initiated research studies. My interest in this area includes computational pathology with artificial intelligence and neural network analysis of cancer histopathology images. Specific applications include improved detection, disease classification, prognosis, and predictive response to therapy.

Ongoing investigations include the integration of AI histopathology with multi-omics analysis to improve disease characterization. Such applications include both solid tumor applications as well as cytopathology. In both applications, the goal is to improve the accuracy and effectiveness of molecular testing and characterization to advance personalized medicine.

Scientifically, the topics of biomarkers and molecular profiling of cancers are of keen interest to me. My research interests include the following:

(i) Understanding the impact of new technologies in the characterization of cancer.

(ii) Reviewing evidence on the molecular biology and biochemical mechanisms associated with cancer onset, progression, metastasis and response to therapy.

(iii) Integrating published information to create new insights into the molecular biology and biochemistry of cancer behavior. These insights help define new concepts and hypotheses for improved diagnostic, prognostic, and predictive medicine applications in cancer-based healthcare.

Current Areas of Research Interest in Personalized Medicine and Cancer.

Currently at the time of this post, there are four areas of cancer research that I am pursuing:

Biochemistry of Inhibitor Therapy in Cancer Treatment.

This area of research investigates the biochemical interaction between specific inhibitory molecules and the target protein (or protein receptor). In cancer, examples of targeted inhibitor therapy includes monoclonal antibody targeted therapy, antibody drug conjugates, and small molecule inhibitors.

The study of the biochemistry of inhibitor therapy provides for a mechanistic understanding disease treatment (mechanism of action).

Studying the molecular biology of drug resistance can provide insights into the mechanisms responsible for the resistance. These studies can often identify the specific genomic and protein alterations that occur during the development of drug resistance and can vary by cancer type and drug treatment. Examples of these genomic and protein alterations include somatic point mutations in the target gene (with corresponding changes in the encoded target protein), changes in expression patterns, chromosomal alterations, and cross-talk between various signal transduction pathways thereby circumventing the specific inhibitor affect. Collectively, these molecular insights can often suggest new therapeutic options for continued treatment or the need for new drug development programs.

Spatial phenotyping.

The basic and clinical research in spatial phenotyping investigates the cellular, protein and RNA expression patterns within tumor microenvironments. This analysis provides new insights into the cellular and molecular interplay between tumor, stroma and immune system. This research provides insight into tumor behavior, prognosis and therapy options.

Spatial phenotyping includes cell clustering analysis within the tumor microenvironment, cell neighbor analysis, cell transcriptomics analysis with cellular location, and protein expression patterns within the tumor and the tumor microenvironment using immunohistochemistry (IHC) analysis.

Artificial Intelligence and Cancer Characterization.

This area investigates the application of artificial intelligence to complex problems in biology and human disease. This application includes the areas of drug development, tumor classification and enhanced disease classification.

Two sub-categories of AI applications in cancer characterization include:

(i) the use of AI approaches to predict cancer behavior, prognosis or response to therapy. These approaches are based upon whole slide image analysis using AI and neural networks.

(ii) the integration of histopathology-based AI analysis with molecular genomic profiling and related multi-omics analysis. The goal of this work is to create a more accurate description of cancer behavior and response to treatment.

Real-World Evidence.

Recently, there has been a trend to publish real-world evidence (RWE) in the peer reviewed literature. Published RWE in cancer cases provides new insights into the routine clinical practice of cancer testing and treatment.

RWE publications offer a unique view into laboratory testing usage, clinical practice patterns, treatment decisions, and patient outcomes.

The use of RWE offers the opportunity to analyze the use of technologies and treatment options in routine clinical practice. RWE does not represent the controlled environment of a pivotal clinical trial results. As such, these data provide new insights into clinical utility that were not measured or the subject of FDA review and approval.

Analysis of published RWE from medical institutions that evaluate the available data from patients receiving standard of care testing and treatment can provide valuable insights into practice patterns, usage, clinical decisions, patient outcomes and reimbursement issues that were previously unknown.

When applied to patients who have failed the first- or second-line therapy options, these publications describe the molecular biology of disease recurrence. These published results help elucidate the underlying drug resistance mechanisms that occur following initial therapy treatment.

Interest in Evidence Review, Analysis and Synthesis in Personalized Medicine and Cancer.

As part of my research into cancer and personalized medicine, I routinely review, analyze and synthesize the published clinical evidence on new approaches in cancer-based personalized medicine.

The goal of this work is multifaceted:

• Review of the published literature and update evidence summaries on the molecular biology of cancer and the mechanisms responsible for the disease process, disease progression, metastasis and drug resistance.

• Analysis of the published literature to create an evidence-based review and critique on clinical utility of these various approaches in cancer characterization and treatment.

• Synthesis of the available information to create a competitive landscape assessment in molecular cancer testing and treatment options available.

• This evidence review work provides for the development of technology roadmaps in personalized medicine. As such, these technology roadmaps can suggest future directions in IVD diagnostic technology, molecular characterization and new therapeutic possibilities in drug development and treatment options.

Future online posts and published digital articles will document my research investigations and interests in the field of cancer-based personalized medicine. These articles will include my review of the published literature and technology advances in cancer based personalized medicine. My literature reviews will also discuss the strength of the available published evidence regarding clinical utility for both current and emerging advances in cancer-based personalized medicine.

To learn about my work, please see the additional details listed below.

My peer-reviewed publications in cancer research can be found here.

My professional career is summarized on my LinkedIn profile which can be found here.

Douglas P. Malinowski, Ph.D.