Good news! Cancer is history (soon)! This is only the beginning!
"... To effectively treat patients with metastatic cancer, physicians must pinpoint where these cells originally came from, but in three to five percent of cases a standard diagnostic workup fails to do so. ... As a result, these cancers often quickly progress, and patient survival is only six to 16 months. ...
Next generation sequencing (NGS) can identify a tumor’s mutations and help determine the cancer type, but the amount of mutation data generated can be too vast for a physician to sift through during an initial diagnosis. ... To address this, ... developed a machine learning model called OncoNPC (Oncology NGS-based Primary cancer-type Classifier) to efficiently sift through large amounts of complex mutation data. ...
Using NGS data from 36,445 tumor samples with known primary cancers, the researchers ran OncoNPC to search for genetic mutations, copy number alterations, and mutational signatures. ... one of 22 different cancer types. After training, they tested OncoNPC on data they previously removed from the training sequence. “OncoNPC managed to correctly identify the origins of known tumors about 80 percent of the time,” ... “When we focused on high-confidence predictions, which made up around 65 percent of all cases, the model's accuracy jumped to an impressive 95 percent,” though it was slightly less precise with rare cancers. ..."From the abstract:
"Cancer of unknown primary (CUP) is a type of cancer that cannot be traced back to its primary site and accounts for 3–5% of all cancers. Established targeted therapies are lacking for CUP, leading to generally poor outcomes. We developed OncoNPC, a machine-learning classifier trained on targeted next-generation sequencing (NGS) data from 36,445 tumors across 22 cancer types from three institutions. Oncology NGS-based primary cancer-type classifier (OncoNPC) achieved a weighted F1 score of 0.942 for high confidence predictions (≥0.9
) on held-out tumor samples, which made up 65.2% of all the held-out samples. When applied to 971 CUP tumors collected at the Dana-Farber Cancer Institute, OncoNPC predicted primary cancer types with high confidence in 41.2% of the tumors. OncoNPC also identified CUP subgroups with significantly higher polygenic germline risk for the predicted cancer types and with significantly different survival outcomes. Notably, patients with CUP who received first palliative intent treatments concordant with their OncoNPC-predicted cancers had significantly better outcomes (hazard ratio (HR) = 0.348; 95% confidence interval (CI) = 0.210–0.570; P = 2.32×10−5
). Furthermore, OncoNPC enabled a 2.2-fold increase in patients with CUP who could have received genomically guided therapies. OncoNPC thus provides evidence of distinct CUP subgroups and offers the potential for clinical decision support for managing patients with CUP."
Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary (no public access, but article above contains link to PDF file)
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