Invention for Methods of selecting treatment regimens in cancer patients and predicting their outcomes

Invented by Nadia Harbeck, Ronald E. Kates, Manfred Schmitt, FOEKENS John A, Biomedica Diagnostics Inc

The market for methods of selecting treatment regimens in cancer patients and predicting their outcomes has seen significant growth in recent years. As cancer continues to be a leading cause of death worldwide, the need for personalized and effective treatment options has become paramount. With advancements in technology and research, various methods have emerged to aid healthcare professionals in making informed decisions about treatment plans for individual patients. One of the key factors driving the market growth is the increasing availability of genomic profiling and molecular diagnostics. These tools allow healthcare providers to analyze a patient’s genetic makeup and identify specific mutations or biomarkers that may influence their response to certain treatments. By understanding the genetic characteristics of a tumor, doctors can tailor treatment regimens to target specific pathways or mutations, maximizing the chances of success while minimizing potential side effects. Another area of growth in this market is the development of predictive analytics and machine learning algorithms. These tools analyze large datasets of patient information, including clinical data, genomic data, and treatment outcomes, to identify patterns and predict the likelihood of response to different treatment options. By leveraging these predictive models, healthcare providers can make more informed decisions about which treatments are most likely to be effective for a particular patient, ultimately improving patient outcomes and reducing healthcare costs. Furthermore, the market for methods of selecting treatment regimens and predicting outcomes is also benefiting from the growing adoption of liquid biopsies. Traditional biopsies involve invasive procedures to obtain tissue samples for analysis. However, liquid biopsies offer a less invasive alternative by analyzing circulating tumor DNA (ctDNA) or other biomarkers found in blood samples. This allows for real-time monitoring of tumor progression and response to treatment, enabling healthcare providers to make timely adjustments to treatment plans. The market for these methods is not without its challenges. One of the main obstacles is the high cost associated with genomic profiling and molecular diagnostics. While the prices of these technologies have been decreasing over time, they still remain relatively expensive, limiting their accessibility to certain healthcare settings or regions. Additionally, the interpretation of genomic data and the integration of predictive analytics into clinical practice can be complex and require specialized expertise, which may pose a barrier to widespread adoption. Despite these challenges, the market for methods of selecting treatment regimens in cancer patients and predicting their outcomes is expected to continue growing. The increasing prevalence of cancer, coupled with advancements in technology and research, will drive the demand for personalized and effective treatment options. As the market evolves, it is crucial for stakeholders, including healthcare providers, researchers, and policymakers, to collaborate and address the challenges to ensure that these innovative methods are accessible and effectively integrated into clinical practice. By doing so, we can improve patient outcomes and move closer to a future where cancer is no longer a devastating disease.

The Biomedica Diagnostics Inc invention works as follows

The present invention is a method for determining the treatment regimen for node-negative or node-positive breast cancer patients. The method involves measuring levels of urokinase type plasminogen activater (uPA), and plasminogen inhibitor-1 (PAI-1), in a subject (preferably a tumour) and, using the values, predicting expected benefits including disease-free and/or overall survivability for the patient, without treatment (beyond surgical removal of tumor tissues) or with particular treatments, and then using this information to select a regimen of treatment for the subject. A high-risk subject has high levels of uPA, PAI-1 and endocrine therapy. Treatment options for high risk subjects include, but are not limited to, adjuvant CMF chemotherapy, adjuvant non-CMF chemotherapy, adjuvant endocrine therapy, adjuvant anthracyclin-containing chemotherapy, radiation therapy, and gene therapy. No treatment, radiation and adjuvant hormone therapy are all options available for subjects with low risk.

Background for Methods of selecting treatment regimens in cancer patients and predicting their outcomes

5.1. “5.1.

5.2. “5.2.

5.3. “5.3.

5.4. “5.4.

5.4.1. “Cancer Treatment Regimens:

5.4.2. “5.4.2.

5.4.3. Therapeutic Antibodies

5.4.4. “5.4.4.

5.5. “Effective Dose

5.6. “5.6.

5.7.1. uPA & PAI-1 proteins for generation of antibodies

5.7.2. “Antibodies to uPA Gene Products and PAI-1 Gene Product

5.7.3. “5.7.3.

5.7.4. “5.7.4.

5.7.5. “5.7.5.

5.7.6. “5.7.6.

5.8. Kits


6.1. Materials and Methods

6.1.1. Patients

6.1.2. “6.1.2. Laboratory Assays

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