Steven P Linke

US Patent:

20090061422, Mar 5, 2009

Filed:

Apr 17, 2007

Appl. No.:

11/787518

Inventors:

Steven P. Linke - La Jolla CA, US
Troy M. Bremer - La Jolla CA, US
Cornelius A. Diamond - La Jolla CA, US

International Classification:

C12Q 1/68
C12Q 1/02
G06G 7/60

US Classification:

435 6, 435 29, 703 11

Abstract:

To maximize both the life expectancy and quality of life of patients with operable breast cancer, it is important to predict adjuvant treatment outcome and likelihood of progression before treatment. The instant invention details the usage of a machine-learning based method to develop a cross-validated model to predict the outcome of adjuvant treatment, particularly chemotherapy treatment outcome, and likelihood of progression before treatment. The model includes standard clinicopathological features, as well as molecular markers collected using standard immunohistochemistry and fluorescence in situ hybridization. The model significantly outperformed the St. Gallen Consensus guidelines and the Nottingham Prognostic Index and has the potential to provide a clinically useful and cost-effective prognostic for breast cancer patients.

US Patent:

20090275057, Nov 5, 2009

Filed:

Mar 29, 2007

Appl. No.:

11/731683

Inventors:

Steven P. Linke - La Jolla CA, US
Troy M. Bremer - La Jolla CA, US
Cornelius A. Diamond - La Jolla CA, US

International Classification:

G01N 33/574

US Classification:

435 723

Abstract:

Colorectal cancer patients with operable tumors must decide whether to receive adjuvant therapy after surgical resection in order to reduce their chances of recurrence. Current clinical guidelines are crudely based on the stage of the disease, as well as a few other clinicopathologic features. The instant invention integrates data from these clinicopathologic features with data on multiple biomarkers using advanced informatic methods to provide a far more accurate prediction of recurrence than the current guidelines. The instant invention consists of a panel of biomarker assays plus an algorithm into which the scored biomarker data, as well as standard clinicopathologic data, is entered. A tumor sample from an individual patient is submitted for test, and an individualized report is produced with a prognostic score that accurately reflects the patient's risk of recurrence. This helps guide the patient and his/her oncologist in their choice of whether to receive adjuvant treatment. Low-risk patients are spared the unnecessary toxicities associated with cytotoxic treatments, and high-risk patients are given the best chance for a cure, maximizing both life expectancy and quality of life.

US Patent:

20110307427, Dec 15, 2011

Filed:

Aug 18, 2011

Appl. No.:

13/199068

Inventors:

Steven Linke - Carlsbad CA, US
Troy Bremer - , US
Comelius Diamond - , US

International Classification:

G06N 5/02
G06F 15/18

US Classification:

706 12, 706 52

Abstract:

Predicting response to adjuvant therapy or predicting disease progression in breast cancer is realized by (1) first obtaining a breast cancer test sample from a subject; (2) second obtaining clinicopathological data from said breast cancer test sample; (3) analyzing the obtained breast cancer test sample for presence or amount of (a) one or more molecular markers of hormone receptor status, one or more growth factor receptor markers, (b) one or more tumor suppression/apoptosis molecular markers; and (c) one or more additional molecular markers both proteomic and non-proteomic that are indicative of breast cancer disease processes; and then (4) correlating (a) the presence or amount of said molecular markers and, with (b) clinicopathological data from said tissue sample other than the molecular markers of breast cancer disease processes. A kit of (1) a panel of antibodies; (2) one or more gene amplification assays; (3) first reagents to assist said antibodies with binding to tumor samples; (4) second reagents to assist in determining gene amplification; permits, when applied to a breast cancer patient's tumor tissue sample, (A) permits observation, and determination, of a numerical level of expression of each individual antibody, and gene amplification; whereupon (B) a computer algorithm, residing on a computer can calculate a prediction of treatment outcome for a specific treatment for breast cancer, or future risk of breast cancer progression.

US Patent:

20060275844, Dec 7, 2006

Filed:

Apr 18, 2006

Appl. No.:

11/407169

Inventors:

Steven Linke - Carlsbad CA, US
Troy Bremer - Dana Point CA, US
Cornelius Diamond - Leucadia CA, US

International Classification:

G01N 33/574
G06F 19/00
G06Q 10/00

US Classification:

435007230, 702019000, 705003000

Abstract:

To maximize both the life expectancy and quality of life of patients with operable breast cancer, it is important to predict adjuvant treatment outcome and likelihood of progression before treatment. A machine-learning based method is used to develop a cross-validated model to predict (1) the outcome of adjuvant treatment, particularly endocrine treatment outcome, and (2) likelihood of cancer progression before treatment. The model includes standard clinicopathological features, as well as molecular markers collected using standard immunohistochemistry and fluorescence in situ hybridization. The model significantly outperforms the St. Gallen Consensus guidelines and the Nottingham Prognostic Index, thus providing a clinically useful and cost-effective prognostic for breast cancer patients.

US Patent:

20170350895, Dec 7, 2017

Filed:

Dec 4, 2015

Appl. No.:

15/529966

Inventors:

- Laguna Hills CA, US
Steven Paul Linke - Carlsbad CA, US

International Classification:

G01N 33/574

Abstract:

The present technology generally relates to methods and compositions relevant to the prediction that a subject with and/or after treatment for DCIS will experience a subsequent ipsilateral breast event that is a DCIS recurrence, an invasive breast cancer, both a DCIS recurrence and invasive cancer, or neither. The technology can assist one with how to treat such subjects.