Managing the genomic revolution in cancer diagnostics

Abstract

Molecular tumor profiling is now a routine part of patient care, revealing targetable genomic alterations and molecularly distinct tumor subtypes with therapeutic and prognostic implications. The widespread adoption of next-generation sequencing technologies has greatly facilitated clinical implementation of genomic data and opened the door for high-throughput multigene-targeted sequencing. Herein, we discuss the variability of cancer genetic profiling currently offered by clinical laboratories, the challenges of applying rapidly evolving medical knowledge to individual patients, and the need for more standardized population-based molecular profiling.

Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells

“…This breadth of information contrasts with the fewer genes included in current cancer mutation panels such as MSK-IMPACT or FoundationOne CDx (468 and 324 genes, respectively), which were designed to be queried manually by a physician (Cheng et al, 2015;Harris, 2017). Moreover, since we currently do not understand the clinical implications of the majority of cancer mutations, there is little consensus on what genes should be included in these pan-cancer mutation panels (Nguyen and Gocke, 2017) or on how physicians should act on the results. An increase in the number of clinically meaningful cancer mutations, facilitated by interpretable machine learning models such as DrugCell, could further motivate the case for complete genomic sequencing of cancer patients (Katsanis and Katsanis, 2013;Kuenzi and Ideker, 2020).…”

Expert consensus on standardized management of tumor gene sequencing for cancer patients

“…In future clinical practice, integrating DNA sequencing and ribonucleic acid (RNA) sequencing can better detect the expression of alleles, evaluate known or unknown gene fusion, confirm splicing bodies, and analyze signal pathways, etc. Compared with adults, it will be more suitable for the detection of pediatric tumors 9 because of the less change of recurrence point mutations. However, RNA sequencing also needs to be further verified in routine diagnosis and treatment due to its huge amount of data and complex analysis.…”

An integrated analysis of the epigenetic, genetic, and transcriptional patterns associated with outcome across cancer types

“…However, it is unknown whether these genes convey prognostic information not already captured by common markers of tumor proliferation, like Ki-67 staining (23). Additionally, targeted and whole-genome tumor sequencing is becoming increasingly routine at many major hospitals (24,25). These sequencing efforts can identify molecular alterations, like BRAF or EGFR mutations, that confer sensitivity to particular therapies, and could shed light on tumor progression (26).…”