Refining the Treatment of Pancreatic Cancer From Big Data to Improved Individual Survival

Abstract

Pancreatic cancer is one of the most lethal cancers worldwide, most notably in Europe and North America. Great strides have been made in combining the most effective conventional therapies to improve survival at least in the short and medium term. The start of treatment can only be made once a diagnosis is made, which at this point, the tumor volume is already very high in the primary cancer and systemically. If caught at the earliest opportunity (in circa 20% patients) surgical resection of the primary followed by combination chemotherapy can achieve 5-year overall survival rates of 30%-50%. A delay in detection of even a few months after symptom onset will result in the tumor having only borderline resectabilty (in 20%-30% of patients), in which case the best survival is achieved by using short-course chemotherapy before tumor resection as well as adjuvant chemotherapy. Once metastases become visible (in 40%-60% of patients), cure is not possible, palliative cytotoxics only being able to prolong life by few months. Even in apparently successful therapy in resected and borderline resectable patients, the recurrence rate is very high. Considerable efforts to understand the nature of pancreatic cancer through large-scale genomics, transcriptomics, and digital profiling, combined with functional preclinical models, using genetically engineered mouse models and patient derived organoids, have identified the critical role of the tumor microenvironment in determining the nature of chemo- and immuno-resistance. This functional understanding has powered fresh and exciting approaches for the treatment of this cancer.

Keywords: CYPY3A; clonal evolution; immunotherapy; molecular subtypes; persister; plasticity; targeted therapies.

Graphical Abstract

The current and emerging status of pancreatic cancer therapy. The left-hand panel shows a schematic representation of the estimated response rates to therapies based on tumor cell mass (the primary tumor and metastasis combined) and time assuming Gomp-Ex tumor cell growth rates. (A mathematical sigmoid function which describes growth as being slowest at the start and end of a given time period was proposed by Benjamin Gompertz in 1825 [Philosophical Transactions of the Royal Society of London]. This was modified for tumor cell growth by TE Wheldon in 1988 [Mathematical Models in Cancer Research], in which the cellular population expands exponentially [Gomp-Ex] on the assumption that initially there is no competition for resources. More complex models are needed to describe metastatic seeding and growth.) The start of treatment can only be made once a diagnosis is made, which at this point the tumor volume is already high in the primary cancer and in micrometastases around the body. If caught at the earliest opportunity (in circa 20% patients) surgical resection of the primary followed by combination chemotherapy can achieve 5-year overall survival rates of 30%–50%. A delay in detection of even a few months after symptom onset will result in the tumor having only borderline resectabilty (in 20%–30% of patients), in which case the best survival is achieved by using short-course chemotherapy before tumor resection as well as adjuvant chemotherapy. Once metastases become visible (in 40%–60% of patients), cure is not possible, palliative cytotoxics only being able to prolong life by a few months. The right-hand panel demonstrates the multiple approaches being undertaken to deepen the understanding of the nature of pancreatic cancer and its responses to different therapies. There is much to be gained from functional preclinical models (genetically engineered mouse models and patient-derived organoids), but the emphasis has shifted to deeper investigations of patient tumors within the context of clever multidisciplinary prospective clinical studies.

Figure 1.

Transcriptomic subtype classifications and surrogate markers of PDAC. Classical tumors are associated with a better patient survival with surrogate markers: GATA6, HNF1A/4A, and PDX. Basal-like tumors have poorer outcomes with surrogate markers: MYC, ΔNTP63, RUNX2, and KRT5/14/17. Hybrid tumors show a mixed character of classical and basal with high plasticity after drug treatment.

Figure 2.

TME subtypes of PDAC tumors. (A) Representative images of Deserted, Intermediate, and Reactive TME subtypes. (B) TME subtype switching from a relatively balanced Deserted/Intermediate/Reactive distribution pattern to a predominant Deserted state after patients have received chemotherapy.

Figure 3.

The PDAC TME showing tumor cell and stromal cell interactions. CCL2/4/5, CC-chemokine ligand 2/4/5; CCR, CC-chemokine receptor; COX2, cyclooxygenase 2; CSF-1, colony stimulating factor 1; CSF-1R, colony stimulating factor 1 receptor; CTLA-4, cytotoxic T-lymphocyte-associated protein 4; CXCL1/12, CXC-chemokine ligand 1/12; CXCR4, CXC-chemokine receptor type 4; DC, conventional type 1 dendritic cell; FGF, fibroblast growth factor; Flt3L, Fms related receptor tyrosine kinase 3 ligand; GAS6, growth arrest-specific protein 6; GM-CSF, granulocyte-macrophage colony-stimulating factor; hnRNPK, heterogeneous nuclear ribonucleoprotein K; HA, hyaluronic acid; HGF, hepatocyte growth factor; IL-1/-6/-33, interleukin-1/-6/-33; MHC-1, major histocompatibility complex 1; PDGF, platelet-derived growth factor; PD-1, programmed cell death protein 1; PD-L1, programmed death-ligand 1; SHH, sonic hedgehog; ST2, suppression of tumorigenicity 2; STAT3, signal transducer and activator of transcription 3; TAM, tumor-associated macrophage; TGF-β, transforming growth factor-β; VEGF, vascular endothelial growth factor; and VISTA, V-domain Ig suppressor of T-cell activation.

Figure 4.

Drug resistance in PDAC patient after cytotoxic chemotherapy. (A) Tumor cells without drug resistance can be completely eradicated after a full (6-month) course of effective chemotherapy. (B) Tumor cell plasticity during chemotherapy results in cells with intrinsic mechanisms of cytotoxic drug resistance (such as CYP3A) to persist and become enriched as the sensitive cell types are killed. (C) Chemotherapy may drive the clonal evolution of cell types to a more Basal-like subtype with new mutations resulting in acquired resistance. Within any one tumor there will be heterogeneity with differential proportions in sensitive and resistant cell types and sub-TMEs, determining the rate of clonal evolution and ultimately survival or death.

Figure 5.

(A) In sensitive PDAC cells following uptake, irinotecan (a component of the FOLFIRINOX and other regimens) is converted to the active metabolite SN-38 by carboxyl esterases, which then kills the cell by inhibition of topoisomerase I. (B) In resistant PDAC cells, CYP3A isoforms 4/5/7 interfere with the metabolism of irinotecan to SN-38 by direct and indirect mechanisms resulting in cell survival. CES: carboxylesterase; CYP: cytochrome P450 isoenzymes; UGT uridine diphosphate glucuronosyltransferase isoenzymes; ABC: ATP-binding cassette transporters, the multidrug resistance-associated protein-1 is encoded by ABCC1; APC: inactive metabolite,7-ethyl-10-[4-N-(5-aminopentanoic acid)-1-piperidino] carbonyl-oxy-camptothecin; and NPC: inactive metabolite 7-ethyl-10-[4-amino-1-piperidino] carbonyl-oxy-camptothecin).