Intratumoral genetic heterogeneity (ITH) poses a significant challenge to utilizing sequencing for decision making in the management of cancer. Although sequencing of multiple tumor regions can address the pitfalls of ITH, it does so at a significant increase in cost and resource utilization. We propose a pooled multiregional sequencing strategy, whereby DNA aliquots from multiple tumor regions are mixed prior to sequencing, as a cost-effective strategy to boost translational value by addressing ITH while preserving valuable residual tissue for secondary analysis. Focusing on kidney cancer, we demonstrate that DNA pooling from as few as two regions significantly increases mutation detection while reducing clonality misattribution. This leads to an increased fraction of patients identified with therapeutically actionable mutations, improved patient risk stratification, and improved inference of evolutionary trajectories with an accuracy comparable to bona fide multiregional sequencing. The same approach applied to non-small-cell lung cancer data substantially improves tumor mutational burden (TMB) detection. Our findings demonstrate that pooled DNA sequencing strategies are a cost-effective alternative to address intrinsic genetic heterogeneity in clinical settings.
Lymph node involvement in renal cell carcinoma (RCC) portends a poor prognosis. However, the role of lymph node dissection (LND) at the time of tumor resection is not fully understood. Conflicting data have been published regarding the survival implications of LND during RCC surgery, and the optimal patient population for which LND might be beneficial has yet to be identified. Based on recent data characterizing the outcomes of node-positive RCC, some have advocated for revising the current staging guidelines to better reflect these findings. Given the paucity of high-quality evidence supporting or refuting the routine use of LND in RCC, further research is needed to shed light on this important topic. There are a number of ongoing clinical trials evaluating the role of perioperative (neoadjuvant and adjuvant) systemic therapy, which include patients with node-positive RCC, and will serve to guide changes in treatment practices for this patient population moving forward.
While multi-level molecular "omic" analyses have undoubtedly increased the sophistication and depth with which we can understand cancer biology, the challenge is to make this overwhelming wealth of data relevant to the clinician and the individual patient. Bridging this gap serves as the cornerstone of precision medicine, yet the expense and difficulty of executing and interpreting these molecular studies make it impractical to routinely implement them in the clinical setting. Herein, we propose that machine learning may hold the key to guiding the future of precision oncology accurately and efficiently. Training deep learning models to interpret the histopathologic or radiographic appearance of tumors and their microenvironment-a phenotypic microcosm of their inherent molecular biology-has the potential to output relevant diagnostic, prognostic, and therapeutic patient-level data. This type of artificial intelligence framework may effectively shape the future of precision oncology by fostering multidisciplinary collaboration.
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