Tempus Labs, Inc.

Tempus Ai, Inc.

Catherine Igartua

Joshua S K Bell

Nike Tsiapera Beaubier

Joshua Drews

Martin Christian Stumpe

Hailey B Lefkofsky

Robert Tell

Ameen Salahudeen

Verónica Sánchez Freire

Yilin Zhang

Raphael A Pelossof

Joshuah Kapilivsky

Kyle Ashley Beauchamp

Calvin McCarter

Timothy Taxter

Benjamin Leibowitz

Aarti Venkat

Jerod Parsons

Lee F Langer

Jackson Michuda

Joshua Sk Bell

Bonnie Victoria Dougherty

Systems and methods are provided for identifying a diagnosis of a cancer condition for a somatic tumor specimen of a subject. The method receives sequencing information comprising analysis of a plurality of nucleic acids derived from the somatic tumor specimen. The method identifies a plurality of features from the sequencing information, including two or more of RNA, DNA, RNA splicing, viral, and copy number features. The method provides a first subset of features and a second subset of features from the identified plurality of features as inputs to a first classifier and a second classifier, respectively. The method generates, from two or more classifiers, two or more predictions of cancer condition based at least in part on the identified plurality of features. The method combines, at a final classifier, the two or more predictions to identify the diagnosis of the cancer condition for the somatic tumor specimen of the subject.

Systems and methods for multi-label cancer classification

Disclosed herein are systems, methods, and compositions useful for determining cellular pathway disruption comprising the use of RNA expression level information. This determined level of disruption can assist in the identification of genetic variants that alter pathway activity, to correlate these variants with disease state and disease progression, and to identify those therapeutics most likely to be effective and which should be avoided.

Systems and methods for detecting cellular pathway dysregulation in cancer specimens

Methods, systems, and software are provided for an ensemble model trained to distinguish between cancers with homologous recombination pathway deficiencies (HRD positive cancers) and cancers without homologous recombination pathway deficiencies (HRD negative cancers) based on nucleic acid sequencing data, e.g., both RNA and DNA sequencing data, generated from a cancerous tissue sample of the subject.

Systems and methods for predicting homologous recombination deficiency status of a specimen

Systems and methods are provided for performing quality control analysis. The method obtains, in electronic form, a batch dataset comprising, for each respective sample in a batch of samples, a corresponding plurality of sequence reads derived from the respective sample by targeted or whole transcriptome RNA sequencing and corresponding metadata for the respective sample. The method determines for the batch dataset a cohort-matched reference batch, where the cohort-matched reference batch is balanced for tissue site, tumor purity, cancer type, sequencer identity, or date sequenced. The method performs one or more global batch quality control tests on the batch dataset using at least the cohort-matched reference batch. The method removes respective samples from the batch dataset that fail any one of the one or more global batch quality control tests or flagging for manual inspection respective samples that fail any one of the one or more global batch quality control tests.

Systems and methods for automating RNA expression calls in a cancer prediction pipeline

Methods, systems, and software are provided for determining a homologous recombination pathway status of a cancer in a test subject, e.g., to improve cancer treatment predictions and outcomes. In some embodiments, classifiers using one or more of (i) a heterozygosity status for DNA damage repair genes in a cancerous tissue, (ii) a measure of the loss of heterozygosity across the genome of the cancerous tissue, (iii) a measure of variant alleles detected in a second plurality of DNA damage repair genes in the genome of the cancerous tissue, (iv) a measure of variant alleles detected in the second plurality of DNA damage repair genes in the genome of a non-cancerous tissue, and (v) tumor sample purity are provided.

Integrated machine-learning framework to estimate homologous recombination deficiency

Growing community of inventors

Chicago, IL, United States of America

Catherine Igartua