Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor type.

Cancer of Unknown Primary (CUP) occurs in 3-5% of patients when standard histological diagnostic tests are unable to determine the origin of metastatic cancer. Typically, a CUP diagnosis is treated empirically and has very poor outcomes, with median overall survival less than one year. Gene expression profiling alone has been used to identify the tissue of origin but struggles with low neoplastic percentage in metastatic sites which is where identification is often most needed. MI GPSai, a Genomic Prevalence Score, uses DNA sequencing and whole transcriptome data coupled with machine learning to aid in the diagnosis of cancer. The algorithm trained on genomic data from 34,352 cases and genomic and transcriptomic data from 23,137 cases and was validated on 19,555 cases. MI GPSai predicted the tumor type in the labeled data set with an accuracy of over 94% on 93% of cases while deliberating amongst 21 possible categories of cancer. When also considering the second highest prediction, the accuracy increases to 97%. Additionally, MI GPSai rendered a prediction for 71.7% of CUP cases. Pathologist evaluation of discrepancies between submitted diagnosis and MI GPSai predictions resulted in change of diagnosis in 41.3% of the time. MI GPSai provides clinically meaningful information in a large proportion of CUP cases and inclusion of MI GPSai in clinical routine could improve diagnostic fidelity. Moreover, all genomic markers essential for therapy selection are assessed in this assay, maximizing the clinical utility for patients within a single test.

Clinical Validation of a Machine-learning-derived Signature Predictive of Outcomes from First-line Oxaliplatin-based Chemotherapy in Advanced Colorectal Cancer.

PURPOSE: FOLFOX, FOLFIRI, or FOLFOXIRI chemotherapy with bevacizumab is considered standard first-line treatment option for patients with metastatic colorectal cancer (mCRC). We developed and validated a molecular signature predictive of efficacy of oxaliplatin-based chemotherapy combined with bevacizumab in patients with mCRC. EXPERIMENTAL DESIGN: A machine-learning approach was applied and tested on clinical and next-generation sequencing data from a real-world evidence (RWE) dataset and samples from the prospective TRIBE2 study resulting in identification of a molecular signature, FOLFOXai. Algorithm training considered time-to-next treatment (TTNT). Validation studies used TTNT, progression-free survival, and overall survival (OS) as the primary endpoints. RESULTS: A 67-gene signature was cross-validated in a training cohort (N = 105) which demonstrated the ability of FOLFOXai to distinguish FOLFOX-treated patients with mCRC with increased benefit from those with decreased benefit. The signature was predictive of TTNT and OS in an independent RWE dataset of 412 patients who had received FOLFOX/bevacizumab in first line and inversely predictive of survival in RWE data from 55 patients who had received first-line FOLFIRI. Blinded analysis of TRIBE2 samples confirmed that FOLFOXai was predictive of OS in both oxaliplatin-containing arms (FOLFOX HR, 0.629; P = 0.04 and FOLFOXIRI HR, 0.483; P = 0.02). FOLFOXai was also predictive of treatment benefit from oxaliplatin-containing regimens in advanced esophageal/gastro-esophageal junction cancers, as well as pancreatic ductal adenocarcinoma. CONCLUSIONS: Application of FOLFOXai could lead to improvements of treatment outcomes for patients with mCRC and other cancers because patients predicted to have less benefit from oxaliplatin-containing regimens might benefit from alternative regimens.

Translocation of a Cell Surface Spliceosomal Complex Induces Alternative Splicing Events and Lymphoma Cell Necrosis.

Spliceosomal dysregulation dramatically affects many cellular processes, notably signal transduction, metabolism, and proliferation, and has led to the concept of targeting intracellular spliceosomal proteins to combat cancer. Here we show that a subset of lymphoma cells displays a spliceosomal complex on their surface, which we term surface spliceosomal complex (SSC). The SSC consists of at least 13 core components and was discovered as the binding target of the non-Hodgkin's lymphoma-specific aptamer C10.36. The aptamer triggers SSC internalization, causing global changes in alternative splicing patterns that eventually lead to necrotic cell death. Our study reveals an exceptional spatial arrangement of a spliceosomal complex and defines it not only as a potential target of anti-cancer drugs, but also suggests that its localization plays a fundamental role in cell survival.