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While AI holds immense potential for accelerating advances in oncology, we must be intentional in developing and applying these technologies responsibly, equitably, and ethically. One path forward is for cancer care providers and researchers to be among the architects of AI and its adoption in medicine. Given the limitations of traditional top-down, hypothesis-driven design in an exponentially expanding data universe, on one hand, and the danger of spiraling into artificial ignorance (ai) from rushing into a purely ‘synthetic’ method on the other, this article proposes a ‘scienthetic’ method that synergizes AI with human wisdom. Tracing philosophical underpinnings of the scientific method from Socrates, Plato, and Aristotle to the present, it examines the critical juncture at which AI stands to either augment or undermine new knowledge. The scienthetic method seeks to harness the power and capabilities of AI responsibly, equitably, and ethically to transcend the limitations of both the traditional scientific method and purely synthetic methods, by intentionally weaving machine intelligence together with human wisdom. 

Nearly seventy percent of diagnostic lab test errors occur due to variability in preanalytical factors. These are the parameters involved with all aspects of tissue processing, starting from the time tissue is collected from the patient in the operating room, until it is received and tested in the laboratory. While there are several protocols for transporting fixed tissue, organs, and liquid biopsies, such protocols are lacking for transport and handling of live solid tumor tissue specimens. There is a critical need to establish preanalytical protocols to reduce variability in biospecimen integrity and improve diagnostics for personalized medicine. Here, we provide a comprehensive protocol for the standard collection, handling, packaging, cold-chain logistics, and receipt of solid tumor tissue biospecimens to preserve tissue viability. 

91 Background: Colorectal cancer (CRC) is the third leading type of cancer worldwide, with ~150,000 new cases in the US annually and a grim 14% 5-year survival for patients diagnosed at a late stage. A lack of treatment options leads to persistently poor prognosis for patients with advanced stage disease. KRAS mutations are well known drivers of CRC and other GI cancers. Multiple KRAS mutations occur in CRC, including G12D (34%), G12V (21%), G13D (20%), G12C (8%), and others (18%). Existing KRAS-targeted therapies have limited use in CRC, underscoring the need for pan-RAS inhibitors in treating CRC and other RAS driven cancers. Objective: Assess activity of ADT-007, our pan-RAS inhibitor, on wild-type (WT) and KRAS-mutant 3D bioprinted organoid tumor (BOT) tissue using our high-throughput ex vivo platform. Methods: Using previously established bioprinting protocols, WT and mutant BOTs were printed with HT29 and HCT116 cells, respectively. HT29 is an established human WT CRC cell line with known sensitivity to proteosome and survivin inhibitors. HCT116 is a KRAS^G13Dmutant human CRC cell line. 3 sets of BOTs were generated and acclimated for 24h. One set was treated for 72h with proteosome inhibitor Bortezomib, another with survivin inhibitor YM155, and the third with our novel pan-RAS inhibitor ADT-007. Dose response curves were generated from both conventional ATP luminescence readouts and high-content imaging. Results: BOT tissue microarchitecture was validated and >200 µm diffusion in BOTs was confirmed using high-content imaging. Differential response was quantified using Cell TiterGlo endpoint assay as well as advanced image processing of high-content live/dead nuclear stained images captured at multiple z-plains. ADT-007 IC₅₀was found to be substantially lower for mutant HCT116 compared to that for WT HT29 cell line BOTs, which was consistent with separately conducted in vitro and in vivo studies. Conclusions: A pan-RAS inhibitor, such as ADT-007 with high selectivity for cancer cells with activated RAS that is not limited to a specific KRAS mutant allele or RAS isozyme, could have broader use for CRC and other RAS-driven cancers. Further, due to their potential to replicate biophysical characteristics of a tumor and its microenvironment, BOT based precision and personalized medicine platforms can provide more accurate drug efficacy readout compared to in vitro cancer models. 

Colorectal cancer (CRC) is a highly prevalent and lethal cancer worldwide. Approximately 45% of CRC patients harbor a gain-in-function mutation in KRAS. KRAS is the most frequently mutated oncogene accounting for approximately 25% of all human cancers. Gene mutations in KRAS cause constitutive activation of the KRAS protein and MAPK/AKT signaling, resulting in unregulated proliferation and survival of cancer cells and other aspects of malignant transformation, progression, and metastasis. While KRAS has long been considered undruggable, the FDA recently approved two direct acting KRAS inhibitors, Sotorasib and Adagrasib, that covalently bind and inactivate KRAS^G12C. Both drugs showed efficacy for patients with non-small cell lung cancer (NSCLC) diagnosed with a KRAS^G12Cmutation, but for reasons not well understood, were considerably less efficacious for CRC patients diagnosed with the same mutation. Thus, it is imperative to understand the basis for resistance to KRAS^G12Cinhibitors, which will likely be the same limitations for other mutant specific KRAS inhibitors in development. This review provides an update on clinical trials involving CRC patients treated with KRAS^G12Cinhibitors as a monotherapy or combined with other drugs. Mechanisms that contribute to resistance to KRAS^G12Cinhibitors and the development of novel RAS inhibitors with potential to escape such mechanisms of resistance are also discussed. 

153 Background: Conventional monolayer cell cultures and xenograft models, while useful and economical in early drug discovery, cannot predict clinical efficacy. Further, preclinical screening assays that rely on differential metabolic activity between separate control and treated wells are incapable of capturing phenotypic response and could overstate efficacy for cells with high rates of proliferation. Consequently, over 95% of anticancer agents that show efficacy in preclinical studies, fail in clinical trials. Recently, patient-derived organoid (PDO) models have been utilized in developing platforms to predict clinical efficacy of preclinical formulations. If successful, such predictive ex vivo technologies could revolutionize cancer treatment by reducing cost and time-to-market for new, more effective therapeutics. Objective: Characterize a novel bioprinted organoid tumor (BOT) high-throughput screening ex vivo platform for drug response prediction (DRP) with known proteosome and survivn inhibitors in colorectal cancer. Methods: Bioink for 3D printing BOTs was prepared with HT-29 cells, an established NCI-60 human colorectal adenocarcinoma cell line with known sensitivity to proteosome and survivin inhibitors. Bioink was deposited layer-by-layer on multiple substrates, in various geometrical configurations, and cured in stages to allow cells and matrix to self-assemble with limited degrees of freedom. BOTs were screened 24h and 48h after printing with proteosome inhibitor Bortezomib and survivin inhibitor YM-155. BOTs were evaluated 48h and 72h after treatment using immunofluorescence live/dead assay. Morphological phenotypic changes resulting from treatment were also recorded. Results: Proteasome and survivin inhibitors have been reported to inhibit proliferation and induce cell death in colorectal cancer cells. A dose dependent response was observed for both agents in our novel BOT HTS thereby validating the platform. In addition, characteristic self-assembly of HT-29 cells was observed to be disrupted at effective doses and at certain concentrations below the effective dose. Traditional ATP assays are incapable of recording such phenotypic modulation. Further, a higher proliferation profile was observed in untreated BOTs suggesting that use of independent control wells in traditional assays could overstate efficacy of treatment. Conclusions: Functional high-throughput ex vivo DRP technologies have the potential to transform cancer treatment – from bench to bedside – along the drug discovery to market roadmap for much needed novel anticancer agents.